Revised: 25 May 2021
Accepted: 22 June 2021
DOI: 10.1002/we.2668
REVIEW ARTICLE
Review of wake management techniques for wind turbines
Daniel R. Houck
Sandia National Laboratories, Albuquerque,
New Mexico 87123, USA
Summary
The progression of wind turbine technology has led to wind turbines being incredibly
Correspondence
Daniel R. Houck, Sandia National Laboratories,
1515 Eubank Blvd. SE, Mailstop 1124,
Albuquerque, NM 87123, USA.
Email: danrhouck@gmail.com
optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power
production, increases structural loading and maintenance, reduces their lifetimes, and
Funding information
US Department of Energy; Wind Energy
Technology Office
ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a
crucial field of research. Herein, four wake management techniques in various states
of development are reviewed. These include axial induction control, wake steering,
the latter two combined, and active wake control. Each of these is reviewed in terms
of its control strategies and use for power maximization, load reduction, and ancillary
services. By evaluating existing research, several directions for future research are
suggested.
KEYWORDS
ancillary services, control, induction, loads, power production, wake, wind turbine, yaw
1
|
THE NEED FOR WAKE MANAGEMENT
Motivations for wake management may simultaneously include power maximization, load reduction and/or distribution, lifetime extension, reduced
maintenance, improved ancillary service capabilities, and, ultimately, reduced levelized cost of energy (LCoE). Wake management has been heavily
researched in the last two decades to identify approaches that can compensate for the negative effects of wakes in existing wind farms and to
improve the design of new wind farms. The growing interest in this research is a direct result of the worldwide installed wind energy capacity, which
has quintupled in the last two decades and is now over 650 GW.1,2 The limited control actuators on a turbine (torque, pitch, and yaw) limit the
options for wake management techniques, but there are many different ways that they may be implemented when considering the design of new
controllers. While it greatly depends on the specifics of the wind farm, implementation of wake management techniques may increase annual energy
production (AEP) by up to a few percent.3,4 While these increases may seem small, a 1% increase in capacity factor of the entire 650 GW fleet, and
assuming $20/MWh, is an increase of over $1.1 billion per year without including turbine lifetime extensions and reductions in maintenance.
Conventional control without wake management works very well when the wind is least aligned with successive downstream turbines so
there is sufficient space between the rows for wake recovery and when the wind speeds are high so turbines are more likely to operate at rated
power (this is further explained in Section 2). Thus, wake management for power maximization is largely a mitigation technique for off-design conditions of lower probability wind directions and/or below rated wind speeds because turbine models and wind farm layout are based on datadriven probabilities of wind directions and speeds. Depending on the site, the conditions for which the wind farm was designed may not happen
very often, and, within a wind farm, rated wind speeds may be rare precisely because wakes from upstream turbines result in lower wind speeds
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2021 National Technology & Engineering Solutions of Sandia, LLC. Wind Energy published by John Wiley & Sons Ltd.
Wind Energy. 2022;25:195–220.
wileyonlinelibrary.com/journal/we
195
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Received: 17 March 2021
HOUCK
within the wind farm. Even if the probability of unfavorable conditions with regard to wind direction and speed is low, the power losses that a
farm can experience due to wakes can be as high as 40%.5 In addition to power losses, turbines that operate in the wakes of others experience
higher loads, require more maintenance, and have shorter lifetimes.6
Wakes are a significant contributor to fatigue loads on a wind turbine, so managing wakes to reduce or more evenly distribute loads can
improve the lifetime of a wind turbine and reduce maintenance requirements potentially without sacrificing power production.3,7 As wind energy
penetration increases, wind farm operators are increasingly being asked by system operators to perform ancillary grid services. These services
include active power control (APC) in which the power production of a wind farm is actively managed to either hold some power in reserve by
operating below its rated power or automatic generation control (AGC) in which a wind farm is asked to follow a reference power signal.8,9 While
wind farms cannot provide inertia to the grid automatically as synchronous generators can, they can be quickly actuated to adjust their production
levels,10 and they can store kinetic energy in their rotors for quick release to aid in frequency regulation.10–13 Finally, with advances in power
electronics, wind turbines are now also able to provide reactive power control to maintain system voltage at least locally.14–16 All of these
objectives—power, loads, and ancillary services—are rarely independent, and two or even all three may be simultaneously optimized by certain
wake management strategies, though trade-offs should be expected.
The most notable of previous reviews related to wind turbine wake management comes from Kheirabadi and Nagamune,2 who focused on
wind farm control for power maximization and who also provide more details on the controllers themselves and the models and simulation tools
used to design them. Boersma et al17 provide an excellent review of wind turbine controls with sections on their connection to wakes and use in
wind farm settings, but considerably less detail on the state of the art in wake mitigation, while Andersson et al18 has a more recent and thorough
classification of wind turbine control schemes and optimization methods for power maximization and grid services. Knudsen et al19 give a review
of topics similar to what is presented herein, though it is briefer and now six years out of date. Given the significant advances in the past six years,
this review provides many updates. Porté-Agel20 have a recent and excellent review covering wind turbine wake dynamics and, in particular, their
interactions with the atmospheric boundary layer. Finally, and most recently, Van Wingerden et al21 released the results of an “Expert Elicitation
on Wind Farm Control.” It is less of a review and more of a survey of stakeholder opinions to evaluate consensus and disagreements on the state
of the art of wind farm controls and what is needed from future research.
What follows is a review of four wake management techniques. The first two, axial induction control (AIC) and wake steering, have received
a great deal of attention from researchers but are still far from perfected. The combination of these is a third option that has only recently been
investigated. Finally, active wake control (AWC) is at the very beginning of its development. After a brief primer on wind turbine control and wake
dynamics, each of the four techniques is discussed in regard to optimizing its control strategy and how it can be used for power maximization, load
reduction, and ancillary services.
2
P R I M E R O N W I N D T U R B I N E C O N T R O L A N D WA K E D Y N A M I C S
|
When the wind passes through a wind turbine, it converts energy in the wind into rotational energy in the rotor that is then converted to
electricity by the generator in the turbine nacelle. This interaction creates a wake downstream of the turbine that is characterized by a velocity deficit and added turbulence. When turbines are placed together in wind farms, their wakes can interfere with each others' abilities to produce rated (maximum) power. While the fluid dynamics of wind energy conversion can be quite complicated, two aspects are fairly
straightforward: first, a wind turbine can only produce its rated power at or above its rated wind speed. Second, higher turbulence levels,
especially when they are asymmetrically distributed across the rotor plane, lead to greater fatigue loads on a turbine.22 Lower wind speeds
and higher turbulence are both key features of a wind turbine wake. Though some loads are proportional to wind speed, turbines operating
in a “waked state” (in the wake of another turbine) may not be able to produce their rated power and may suffer from additional fatigue
loads due to turbulence.
2.1
|
Wind turbine control
An individual wind turbine has three control actuators: blade pitch angle (β), generator torque (K), and yaw angle (γ). Changing these individually or
in combination can change the induction factor, a, which is
a¼
U∞ Udisc
,
U∞
ð1Þ
where U∞ is the freestream or ambient wind speed at hub height and Udisc is the wind speed at the rotor disc. The induction factor can be related
to both the thrust coefficient, Ct, which is
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
196
F I G U R E 1 An example of the relationships between blade pitch, TSR, and the thrust and power coefficients of a wind turbine. The “+” marks
the design operating point for maximum power production. Plots made from data of Sandia National Laboratories' National Rotor Testbed
Rotor23
Ct ¼ 4að1 aÞ,
ð2Þ
CP ¼ 4að1 aÞ2 :
ð3Þ
and the power coefficient, CP, which is
These represent thrust force of and power produced by the turbine non-dimensionalized by the force of and power in the wind over the rotor
disc area, respectively, and are derived from an idealized actuator disc analysis of a wind turbine.22 Finally, changing β or K can affect the tip speed
ratio (TSR), which is
TSR ¼
ωD
,
2U∞
ð4Þ
where ω is the rotation rate of the rotor and D is its diameter. The relationship among some of these is seen in Figure 1.
The conventional control scheme for a wind farm is known as greedy control and uses maximum power point tracking (MPPT).24 In this
scheme, every turbine is independent and agnostic of other turbines. Each turbine uses inputs from several sensors to determine the wind direction and the turbine's current operational state, in particular rotor speed, as inputs in a control algorithm that determines the best control to maximize power production unless the operator overrides it.25 It should be noted in Figure 1 that a higher or lower TSR or pitch will result in less
power (lower CP). Also, CP is less sensitive around its peak to changes in TSR and pitch than Ct.26
2.2
|
Wake dynamics
The magnitude of a wake and how quickly it dissipates are mostly related to the wind speed and ambient turbulence level. The “size” of a wake is
often characterized by its velocity deficit, VD, which is
VDðx, y, zÞ ¼
U∞ uwake ðx,y, zÞ
,
U∞
ð5Þ
where uwake is the wind speed anywhere in the wake. The wind speed determines in what region of a turbine's capacity curve it is operating (see
Figure 2). In Region 2, the wind speed is between cut-in and rated, and the turbine is trying to maximize power using generator torque control to
maximize its thrust. Here, wakes are strongest because there is no surplus energy in the wind; i.e., the turbine is using as much of the available
energy as it can. Considering Equation (5), the velocity deficits during Region 2 operation are larger because U∞ is lower. In Region 3, when the
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
197
HOUCK
HOUCK
FIGURE 2
An example of a capacity curve showing how the power output, CP, and Ct change with wind speed. Data from Kelley23
turbine reaches rated power, a turbine will use pitch control to maintain a constant rotor speed, which keeps it at its design and optimal TSR. This
is now primarily a limiting control in that it has already achieved maximum power and is maintaining an operating point that keeps it within the
limitations of the generator and power electronics. Wakes during Region 3 operation produce smaller velocity deficits (higher uwake) because proportionally less energy is harvested from the inflow than is available.17,25 Though operating in Region 3 is the design operating point, Region 2 is,
in fact, the most common operating regime for most turbines in wind farms either because of turbines in waked states or site specific wind conditions.27 Region 2.5 is a transitional region in which some turbines achieve their rated speed before achieving rated power.
Turbulence is the main mechanism by which wakes decay. It stirs the wake with the ambient flow until the two are homogeneous again.
Turbulence intensity (TI) is very significant in wake dynamics and is defined as
σU
TI ¼ ,
U
ð6Þ
is its temporal mean. Higher TI accelerates wake recovery whether it origiwhere σ U is the standard deviation of the streamwise velocity, U, and U
nates in the freestream or is added by the turbine.22
An additional aspect important to the fluid dynamics of wake formation and decay is the shed vorticity from the turbine rotor and nacelle.
Particularly, each rotor blade sheds a continuous vortex from its tip that spirals in a helix downstream. The distance between successive helices is
called the pitch and is inversely proportional to the rotation rate (i.e., the higher the rotation rate, the lower the pitch). In the near wake, the tip
vortices are stable to perturbations and effectively shield the ambient flow from significant mixing with the wake. As the vortices interact with
the ambient flow and each other, they eventually destabilize and begin to breakdown, which allows for further mixing between the wake and the
ambient flow and causes further breakdown of the vortices. Higher turbulence will accelerate this process.28–31 As this process is related to
the rotor speed, it indicates that how a turbine is operated affects how its wake is formed and decays.
3
3.1
WAKE MANAGEMENT TECHNIQUES
|
|
Axial induction control
AIC involves modifying the induction factor of upstream turbines to the benefit of downstream turbines. The name AIC is misleading as the induction factor is not directly controlled on the wind turbine. Rather, the induction factor can be indirectly controlled by adjusting the blade pitch or
the generator torque. One of the primary difficulties in understanding literature regarding AIC is that, depending on the study, researchers may
have implemented AIC by changing a, β, K, TSR, Ct, or CP, and it is clear that these do not all have the same effect on the dynamics of a turbine's
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
198
TABLE 1
Summary of studies of axial induction control
Reference
Abbes and Allagui
32
Focus
Method
Control parameter(s)
PM
Wake model
TSR
Adaramola and Krogstad33
PM
Wind tunnel
β, TSR
34
PM
Wake model
CP
Ahmad et al35
PM
Field test
CP
36
PM
Wake model
CP
Annoni et al37
PM
Wake model, CFD
Wake expansion angle, β, K
Barradas-Berglind and Wisniewski38
PM, LE
BEM
a
Bartl and Sætran39
PM
Wind tunnel
β, TSR
Bartl
PM
Wind tunnel
β, TSR
Behnood et al41
PM
Wake model
β, TSR
PM
Wake model
a
PM, LE
Field test
β
PM
Wake model, field test
β, K
Ahmad et al
Ahmad et al
40
Bitar and Seiler
Boorsma
42
43
Bossanyi and Ruisi44
PM, LE
CFD
β
Bubshait et al46
AS
Wake model
power
Campagnolo et al47
PM, LE
Wind tunnel
β
Ceccotti et al48
PM
Wind tunnel
TSR
Brand et al
45
Chhor et al
49
PM
Wake model
Ct
Ciri et al50
PM
CFD
K
Cole et al51
LE, AS
Wake model
K
PM
Wind tunnel
β
PM
Wake model
a
PM
Wake model
TSR
PM
CFD
β
LE, AS
CFD
β, K
PM, LE
BEM, wind tunnel
β
PM, LE
BEM
TSR, Ct
AS
BEM
K
PM
CFD
β, K
PM
Wake model
β, TSR
AS
Field test
K
AS
CFD
β
AS
Field test
K
PM
Wake model
a
PM
CFD, Field test
β
Corten and Schaak
52
Dam et al53
De-Prada-Gil et al
54
Dilip and Porté-Agel55
Fleming et al
9
Frederik et al56
Galinos et al
57
Gao et al58
Gebraad et al
26
González et al
59
Gravagne et al11
Guggeri et al
60
Guttromson et al12
Herp et al
61
van der Hoek et al62
Horvat et al
63
PM, LE
Wake model
TSR
Johnson and Thomas64
PM
Wake model
a
Johnson and Fritsch65
PM
Wake model
a
PM, LE
CFD
β
Kayedpour et al66
PM
Wake model
wake expansion angle
Kazda et al67
PM
CFD
CP
Kiani et al68
PM
Wake model
a
69
PM
Wake model, wind tunnel
β
Kondo and Inage70
PM
Wake model
CP
PM, LE
CFD
β
PM
Wake model
β
Kanev et al
Kim et al
3
Kucuksahin and Bot
Lee et al72
71
(Continues)
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
199
HOUCK
HOUCK
TABLE 1
(Continued)
Reference
Focus
Method
Control parameter(s)
13
AS
Wake model
K
Lyu et al73
AS
Wake model
β, K
PM
Wake model
Ct, TSR
PM
Wake model
a
PM, LE
CFD
K
PM
Wake model
β, TSR
PM
Wake model
a
PM
Flume
TSR
AS
Wake model
K
PM, LE
CFD
TSR
PM
Wake model
β, TSR
Lee et al
Ma et al
74
Marden et al75
Martinen et al
76
Mirzaei et al77
Park and Law
78
Okulov et al79
Rijcke et al
80
Santhanagopalan et al81
Serrano González et al
Silva et al
82
83
LE, AS
CFD
K
Su et al84
LE, AS
Wake model
power
Tian et al85
PM
Wake model
β, TSR
Tian et al14
AS
Wake model
power
Vali et al86
PM
Wake model
a
Vali et al87
LE, AS
CFD
a
88
Vali et al
LE, AS
CFD
a
Vali et al7
LE, AS
CFD
a
Van Binsbergen et al89
PM. LE
Wake model
β
PM
CFD
Ct
PM
Wake model, BEM, wind tunnel
CP, K
PM, AS
Wake model
β
PM
Wake model
K
PM
Wake model
β, TSR
Vitulli et al
90
Wang and Garcia-Sanz91
Wang et al
16
Yang et al92
Zhang et al
93
Note: The focus of the study is broadly defined as power maximization (PM), load effects (LE), and/or ancillary services (AS). The method refers to how the
wind turbine wake was generated in the study (wake model, field test, etc.). The control parameter is the variable that was used in the study to derate the
turbine(s).
wake. AIC should also be distinguished from sector management and AWC (see Section 3.4). The former may be considered a rudimentary
unoptimized version of AIC in which some turbines are simply shut down to avoid wake effects within an array. The latter involves periodic, or
dynamic, changes to a turbine's operating point. AIC, on the other hand, is intended to be relatively static and only vary on long time scales to perhaps adjust for very large changes in wind speeds or directions. A summary of AIC studies can be found in Table 1.
Generally, the intended effect of AIC is to minimize the impact of upstream wakes by reducing their velocity deficits. If an upstream turbine
produces less power than it could, it leaves more energy in its wake for downstream turbines by virtue of the higher wind speeds in its
wake.42,52,64 Use of AIC may be most applicable during Region 2 operation due to the ability to mitigate more significant wakes, though Region
3 operation provides a greater range for derating. Unless a wind farm has very few rows or very large downstream spacing, downstream turbines
may frequently experience below rated wind speeds due to upstream wakes while only the most upstream turbines operate at rated power.
When implementing AIC, most studies find that the setpoint, or attempted power production, of turbines should be raised from a low value
for the most upstream turbine(s) to its peak, or optimum, for the farthest downstream turbine(s). In this way, the available energy is redistributed
such that upstream turbines do not produce as much as they could at their optimal setpoint, but downstream turbines produce more than they
would using MPPT and their additional power compensates for the power lost by derated upstream turbines. The exact arrangement of setpoints
within the farm must be predetermined or actively calculated by a control algorithm.
As reviewed by Kheirabadi and Nagamune,2 the resulting power gains from implementing AIC in experiments have been inconsistent and
appear marginal at best. The majority of studies are low-fidelity simulations and analytical studies that use one of a few popular wake models to
approximate the dynamics of wake–turbine and wake–wake interactions.32,34,36–38,41,42,45,49,53,54,59,61,63,65,66,68,70,74,75,77,82,85,89,91,93,94 Some
have used high-fidelity computational fluid dynamics (CFD)3,9,26,37,60,62,67,76,81,86,90 or specifically large eddy simulations (LES),50,55 as well as
scaled wind tunnel experiments,33,39,40,47,48,52,56,69,79,91 and field tests.35,43,44,62,71 The potential of AIC may have been inflated by the high
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
200
number of low-fidelity simulations, which likely report high power gains because they lack sufficient detail to capture critical wake dynamics. The
majority of low-fidelity wake models do not include the effects of thrust on the wake, and they may not include the effects of rotor-added turbulence.95 For many, the wake decay rate is an input. This parameter is highly sensitive to environmental conditions such as ambient turbulence,
topography, and atmospheric stability96 and can drastically change the results of simulations.61
Annoni et al37 demonstrated the reason these approximations do not work well for studying AIC. They found that the energy left in the wake
of a derated turbine is not advected downstream within its wake but is along the edges of the wake. As the wake expands, any turbine positioned
directly downstream will not be able to harvest this excess energy. Another reason that so many studies may be inadequate indicators of AIC's
potential is that they test it with a single column of turbines aligned to the inflow.26,33,35,37–40,42,48,50,55,56,59,62,63,65,66,68–70,74,76,77,79–82,85,89,91,92,94
While such an arrangement does provide a worst-case scenario as a baseline, it may also be a worst-case scenario for AIC because downstream
turbines are not optimally placed to harvest the excess energy left by upstream-derated turbines.
For the success of AIC, the transverse spacing of turbines may be as important as the downstream spacing. A turbine that is downstream and
slightly offset from an upstream turbine that is being derated may be ideally positioned to harvest the excess energy left in the wake. If downstream spacing is large, a wake mitigation technique is less necessary and so less effective.49,59 Similarly, if wake recovery is already fast due to
high TI, AIC will be less effective.55 While most studies of AIC in arrays of turbines are low-fidelity because of the computational efficiency, these
studies indicate that the power gains from AIC are approximately proportional to the size of the farm.78 They also indicate that power gains will
be larger from AIC when turbines are aligned with the flow as opposed to staggered, which is the same as looking at differences in wind direction.78,82 That gains could be higher when turbines are aligned with the wind may sound contrary to criticisms above regarding testing AIC with a
column of turbines, but, in an array, there are turbines to the sides that can harvest the available energy in the wakes of upstream turbines given
enough downstream distance for the wakes to expand and mix with each other and the ambient flow. In an aligned array, AIC may show large
power gains relative to a worst-case baseline.
3.1.1
|
Control methods
The best method for implementing AIC has yet to be determined. When implementing AIC, while the deficit in the wake may be reduced and result
in higher wind speeds downstream, the rotor-added turbulence is also reduced when thrust is reduced, which reduces mixing and prolongs wake
decay.37 The net effect on the turbine's wake may be null due to this. This assumes that AIC is implemented by reducing the thrust of the upstream
turbine. Alternatively, some studies have shown that increasing the thrust beyond optimal may help the wake recover more quickly by simultaneously lowering power and adding turbulence.97–99 This suggests that the way in which a turbine is derated is important to the success of AIC.
AIC must be implemented with torque or pitch control, or both, as these can both be directly controlled on a full-scale turbine. As mentioned,
torque and pitch control can both affect the induction, power, thrust, TSR, and/or rotation rate, and some studies vary these instead, though they
cannot be directly controlled on a full-scale turbine. Torque control and TSR are sometimes understandably conflated as the goal of pitch control
is usually to maintain a constant TSR, while torque control adjusts it, though TSR is a derived quantity and not a control input. Because they are all
potentially interdependent, it is difficult to judge results of studies that do not directly reference pitch or torque control and to know precisely
how results could be replicated at full scale. For example, Figure 3 shows a pitch versus TSR plot with a constant CP curve and five possible
F I G U R E 3 A contour curve of constant CP as a function of pitch and TSR showing five different possible ways of achieving it when derating
from the maximum CP. Data from Kelley23
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
201
HOUCK
HOUCK
operating points. The same CP may be achieved by changing TSR and keeping pitch constant, changing pitch while keeping TSR constant, maximizing the rotation rate using both pitch and TSR, or by minimizing or maximizing thrust using both pitch and TSR. If an experiment indicates that the
CP was used as the variable to implement AIC without additional information, one can only assume how to replicate it with typical controller
inputs. The contrast in results due to different AIC implementations that was highlighted above emphasizes the need for more detailed information regarding what control variables are altered and how.
The limited number of high-fidelity experiments with realistic controls leaves the question open as to the best implementation of AIC at full scale.
It appears important to consider the effect on Ct and its effect on the wake, but TSR may also be important. Some studies have shown that higher
TSRs may accelerate wake decay because the decreased pitch between tip vortex helices causes them to interact more and break down sooner,
which accelerates mixing with the ambient flow.28,100 Finally, as every turbine model is different, not just in its control ranges but, perhaps importantly, in the distribution of induction along its rotor span, the ideal implementation of AIC may need to be uniquely determined for each turbine.
The final question regarding implementation of AIC is the optimal implementation in a wind farm. Using mostly wake models, studies have
optimized array power with strategies including genetic algorithms,59,72,77,82 extremum seeking control,50,65,92 and particle swarm optimizations34,36,41,70,85,93 among other techniques. These optimizations vary between static optimizations based on constant inputs (e.g., constant wind
speed) and actively updated optimizations using models to determine the optimal update with unsteady inputs (e.g., unsteady wind speed). If and
when the success of AIC is shown to be more certain, how to best implement it in terms of controlling a wind farm will be an important next step.
3.1.2
|
Power maximization
Of over 70 studies reviewed, only four meet the following criteria: AIC used for total power maximization, an array of turbines as opposed to a
single column, a mid- or high-fidelity method, and a clear indication of the control method. Kazda et al67 used a Reynolds-averaged Navier–Stokes
(RANS) solver with an immersed boundary method to conduct CFD simulations of the Mont Crosin wind farm. Their analysis focused on two pairs
of aligned turbines offset from each other with parallel alignments. They compared conventional operation to derating the upstream turbine of
each pair to 62.5%, 75%, or 87.5% of its peak power production by varying both its pitch and TSR and simulated these for two below rated wind
speeds with a TI of 13%. They found that all deratings increased the wind speed in the wakes of the derated turbines. Considering just these four
turbines, the highest power gain of 13% was achieved when the upstream turbine of each pair was derated to 87.5% of its peak power during the
lower of the two wind speeds. A slightly higher wind speed reduced this gain to 9.7%. While the authors do not offer this explanation, it seems
likely that the offset of the two pairs was particularly important to the success of AIC in that the downstream pair was better positioned to harvest the excess energy left in the wake of the upstream, derated turbine.
Kanev et al3 used the Energy Research Center of the Netherlands' (ECN) FarmFlow software, which has been validated against data from several wind farms. Their optimization scheme determined the pitch angle of only upstream, unwaked turbines to maximize annual power. They argue
that turbulence levels within a wind farm are usually high enough to facilitate rapid wake recovery and derating beyond the most upstream row has
little effect. This optimization was simulated using the layouts and wind resource data of several wind farms. They found that the AEP increases
using pitch-based AIC range from 0.024% to 0.62%. As no error analysis is provided, it remains possible that these gains are within uncertainty.
Using a linearized CFD RANS method and the wind resource data for the Lilligrund wind farm, Vitulli et al90 tested an open-loop controller. A
test case demonstrated that the optimal control minimized the thrust of derated turbines. With this result, they collapsed the design space into
a lookup for the pitch and TSR that achieved a given CP with minimized thrust. They implemented this simplified controller with the Lilligrund data
using the mean wind speed in each 30 sector. Over the entire wind rose and all observed wind speeds, the AEP increased about 1%. The largest
increases occurred in the most aligned wind directions and with the lowest wind speeds.
Finally, a wind tunnel study by Campagnolo et al47 used an array of 2 3 turbines spaced 4D in the streamwise direction and 7D in the transverse. It should be noted that it is somewhat reversed for the downstream spacing to be smaller than the transverse spacing. Using a uniform
inflow with low turbulence and below rated wind speeds, the pitch of the first row was gradually increased, while power was measured to track
the changes by row. They found only a marginal power gain of 0.9% when considering only the front two rows and no gain when considering all
three rows. While the power in the second row increased enough to compensate for the reduced power in the first row, the power in the third
row was reduced to an extent that it nullified this gain. Notably, the particularly wide transverse spacing and small streamwise spacing of their
array may mean that the downstream turbines were not optimally positioned to harvest energy at the edges of the upstream turbines' wakes and
the upstream wakes did not have sufficient distance to expand to reach offset turbines downstream.
3.1.3
|
Effects on loads
An additional benefit of AIC beyond power maximization is its potential to reduce loads on turbines, both those that are derated and those in the
wakes of derated turbines. Some studies have even evaluated AIC primarily as a way to reduce or more evenly distribute loads within a wind farm
as opposed to using it for power maximization.7,83,84,88
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
202
The simulations by Kanev et al3 mentioned earlier also included a mixed optimization for power maximization and load reduction. By evaluating tower, rotor, blade, and shaft loads, they determined that the lifetimes of three of the simulated wind farms could be extended by as much as
1.46%. Their optimization does not account for the changes in loads due to the optimized controller, but it is conservative because the lifetime
extension is based on the lowest load reduction of each component per turbine.
Vali et al.7,87,88 performed three optimizations using LES. First, they demonstrated a control scheme that allowed a wind farm to follow a reference power signal while more evenly distributing thrust loads among the turbines.87 Then, they demonstrated a control scheme that can maintain power production levels of a greedy control scheme while redistributing the load levels from turbines that are highly loaded due to wakes to
other, less loaded turbines.88 Finally, this work was extended to a larger wind farm to further validate their closed-loop controller and to show that
it exhibits greater ability than conventional control to track a reference signal while more evenly distributing turbine loads. The controller reduced
the standard deviation of the normalized damage equivalent loads (DELs) by more than half of the baseline case.7 The latter two studies are based
only on tower base fore-aft bending moment.
If curtailment (reduced power production) of the entire wind farm is considered, then there is additional flexibility among turbine operating points to minimize loads. Su et al84 demonstrated an optimization that more evenly distributed fatigue loading within a wind farm whenever power production was curtailed below rated power. One advantage of such a scheme is that it allows for more predictable and regular
maintenance schedules. Similarly, but with only two turbines, Galinos et al57 used a high-fidelity aeroelastic simulation to evaluate different
derating strategies when the power of the upstream turbine was curtailed by 20% or 40%, with the turbines 4D or 7D apart, at a below rated
or above rated wind speed, and varying the wind direction ±15 on either side of being directly aligned with the turbines. They found that
derating the upstream turbine by either minimizing its rotor speed or minimizing its thrust (which were the same in some scenarios) always
reduced the fatigue loads of the blade root, tower base, and nacelle yaw bearing up to 7% more than derating while maximizing the rotor
speed.
3.1.4
|
Ancillary services
Because all ancillary services relate directly to power production and results are greatly affected by the fidelity of the method when AIC is
implemented, the review in this subsection is limited to high-fidelity studies. Fleming et al9 performed CFD simulations of a 3 3 wind farm
spaced 5D in both directions to evaluate the ability to use APC for AGC with either torque or pitch control. When the wind direction minimized wake interference, torque control was very successful at following the reference power signal and simultaneously reduced blade-flap
and tower fore-aft DELs, though it increased the drivetrain torque DEL. In cases when the upstream wakes impinged upon downstream turbines, it was impossible to successfully follow the reference regardless of the control method. Because they were not waked, the first row of
turbines was fairly successful at following the reference power signal, especially with larger reserves, but waked turbines could not track the
signal without additional information. Vali et al7 had greater success in tracking a reference signal with different reserve levels with their
closed-loop control scheme that also more evenly distributed loads on the turbines, but they only ran their LES with a single wind direction
through a staggered farm layout.
While it is necessary to consider the results more cautiously due to the use of wake models, which may not capture the effects of changing
thrust and/or TSR, as opposed to the high-fidelity CFD methods used above, other studies have optimized wind farms for frequency
regulation,11–13,46,51,58,73 reducing power gradients,80 and providing reactive power dispatch16 using some form of AIC. It is likely that all of these
capabilities have great potential with AIC, but future work using high-fidelity methods that accurately represent wind turbine wake dynamics and
their effects on power production are needed.
3.1.5
|
Summary
AIC holds limited potential for providing often complementary benefits of power maximization, load reduction, and ancillary services by strategically derating particular turbines within an array of turbines. Previous research provides occasional reasons to be optimistic, but it suffers from a
dearth of high-fidelity simulations and especially field tests. There is also no clear indication of best practices for AIC implementation in an individual wind turbine regarding optimal control mechanisms or within a wind farm regarding optimal control methods. Without online models or
turbine–turbine communication, an optimized AIC scheme requires knowledge from previous experiments to properly implement. Existing lowfidelity models may not be accurate enough to produce a lookup table that could be added to the control scheme and producing such a table with
high-fidelity models may be too computationally expensive. As best as can be discerned from available literature, power gains using AIC are
expected to be in the low single digit percents and load reductions slightly higher, or less when taken over all wind directions and the lifetime of a
wind farm. It is difficult to quantify AIC's benefits with regard to grid services, and they may come more in the form of allowing greater wind
energy penetration and participation levels as these services are expanded.
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
203
HOUCK
HOUCK
3.2
Yaw control/wake steering
|
Research into yaw control for wake steering dates back at least two decades but has advanced rapidly in the last 5 years or so. Since turbines in
waked states produce less power and experience greater loads, steering upstream wakes away from them may mitigate these issues. Newton's
third law says there must be an equal and opposite reaction for the thrust a turbine imparts to the wind field.101 That reaction is the wake, and it
is opposite to the rotor in both its direction of swirl and its angle to the inflow (see Figure 4). Because turbine rotors rotate in only one direction,
it is important to use a consistent sign convention when referencing the yaw angle. Most literature and this review follow the right hand rule and
consider a counterclockwise yaw when looking down from above to be positive. Accordingly, if the turbine is yawed positively, the wake will be
skewed negatively and vice versa. A summary of wake steering studies can be found in Table 2.
Though there is good agreement on some aspects of wake steering such as optimal angles and directions of yaw, it is also clear that there are
several variables that can influence the results. A simple way to consider the effects of wake steering is to track the center of the steered wake
and measure its distance from the center of the next turbine downstream, though methods to determine the center of a
wake vary.104,109,119,138,144,153,155,164 If the goal of wake steering is to minimize overlap with a downstream turbine, it requires the wake center
of the yawed turbine to be displaced at least a diameter from where it is aligned with the downstream turbine, though likely more due to wake
expansion. Partial overlaps can still be beneficial in terms of power production but are more complicated in terms of loading (see Section 3.2.3).
There are many factors affecting the amount of wake deflection relative to downstream turbines. These include both the downstream and
transverse spacings, the yaw misalignment angle, and the ways in which various ambient conditions affect the wake spread rate. Shorter downstream spacings give the wake less distance to expand, which may allow for smaller misalignment angles, but also less distance over which to be
steered away. Similarly, wind directions slightly off the aligned direction or small offsets in transverse spacing with respect to the aligned direction
will decrease wake overlap without wake steering and so require smaller misalignment angles to avoid overlap. The effects of turbulence are not
straightforward. Low turbulence reduces the wake decay rate, which enhances the gains from wake steering because the baseline of full wake
overlap is worse. Because low TI also reduces wake spread rates, it allows for smaller misalignment angles. Higher TI will enhance wake decay and
spread the wake faster and require higher misalignment angles to avoid overlap. High TI also causes the wake as a whole to meander more,
unpredictably increasing or decreasing the intended offset.138 Other atmospheric conditions such as shear and veer further complicate the
issue.110,142 Optimal yawing is likely particular not just to each wind farm but also strongly dependent on conditions.142 This section is devoted to
relatively static wake steering that varies only slowly primarily in response to changes in wind direction. For dynamic yaw control, see Section 3.4.
3.2.1
|
Control methods
While many studies have performed proofs of concepts using only two or three turbines,26,33,40,47,102,106,109,111,112,119,123,125,131,132,134,138,143,151,161–164
wake steering is more complicated in a wind farm. Fortunately, and in contrast to AIC, many of the lessons learned with a column of turbines still
FIGURE 4
A simplified view of the difference in wake propagation direction due to yaw misalignment with the inflow
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
204
TABLE 2
Summary of studies of wake steering
Reference
Adaramola and Krogstad,
33
Focus
Method
PM
Wind tunnel
Ahmad et al102
PM
Field test
36
PM
Wake model
PM
Wake model
PM
CFD
PM
Field test
PM, LE
Wind tunnel
wake
Wind tunnel
PM
Wind tunnel
PM
Field test
wake
Field test
PM, LE
Wind tunnel
PM
Wind tunnel
Ahmad et al.
Annoni et al. 103
Archer and Vasel-Be-Hagh
104
Astolfi et al105
Bartl et al.
106
Bartl et al107
Bastankhah and Porté-Agel
Bromm et al109
110
Brugger et al.
Campagnolo et al
47
Campagnolo et al111
Campagnolo et al
112
108
PM, LE
Wind tunnel, wake model
Churchfield et al113
PM, LE
CFD
114
PM, LE
CFD
PM
CFD
LE
CFD
Ciri et al
Cossu115
Croce et al
116
Dahlberg and Medici 117
PM
Wind tunnel
Damiani et al118
LE
BEM, field test
Dhiman et al119
PM
Wake model
van Dijk et al. 120
PM, LE
BEM, wake model
Doekemeijer et al121
PM
Wake model, field test
Doekemeijer et al122
PM
CFD, wake model
Draper et al
123
PM
CFD, wind tunnel
Ennis et al124
LE
Field test
Fleming et al. 125
PM, LE
CFD
Fleming et al126
PM, LE
CFD
Fleming et al
127
PM, LE
CFD
Fleming et al
128
PM
Wake model
Fleming et al9
LE, AS
CFD
Fleming et al129
wake
CFD, field test, wake model
Fleming et al130
PM
Field test, wake model
131
PM
CFD, wake model
Fleming et al132
PM
Field test, wake model
133
PM
Field test
Fleming et al
Fleming et al
Fortes-Plaza et al134
PM
CFD
Frederik et al56
PM, LE
BEM, wind tunnel
Gebraad et al135
PM
CFD
Gebraad et al26
PM
CFD
PM, LE
CFD, wake model
PM, LE
CFD, wake model
PM, LE
Field test
PM
CFD, wake model
140
PM
Field test
Howland et al141
PM
CFD, wake model, field test
Gebraad et al
136
Gomez-Iradi et al137
Herges et al
138
van der Hoek et al139
Howland et al
(Continues)
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
205
HOUCK
HOUCK
TABLE 2
(Continued)
Reference
Focus
Howland et al
142
Method
PM
Wake model, field test
Hulsman et al143
PM, LE
CFD, wake model
Jimenez et al144
wake
CFD, wake model
Kanev et al3
PM, LE
CFD
Kanev
145
PM
CFD
Kanev et al146
PM, LE
Wake model
King et al4
wake
Wake model
Kragh and Hansen147
PM, LE
Wake model
PM, LE, AS
Wake model
PM
Wake model
PM
Wake model, field test
PM, LE
CFD, wind tunnel
PM, LE
LES
wake
Wind tunnel
PM
Field test
PM
CFD
PM
CFD, wake model
157
PM
Wake model
Raach et al158
PM
Wake model
Schreiber et al159
PM
Wake model
wake
Wake model
PM
Wake model
PM, LE
CFD
PM, LE
BEM
PM
Wind tunnel, wake model
Kretschmer et al
148
Kuo et al149
Lima et al
150
Lin and Porté-Agel
151
pez et al152
Lo
Macri et al
153
Mckay et al154
Miao et al
155
Qian and Ishihara156
Quick et al
Shapiro et al
160
Simley et al161
Uemura et al
162
Zalkind and Pao163
164
Zong and Porté-Agel
Note: The focus of the study is broadly defined as power maximization (PM), load effects (LE), and/or ancillary services (AS), or the study may have focused
on the wake of the controlled turbine (wake). The method refers to how the wind turbine wake was generated in the study (wake model, field test, etc.).
The control parameter(s) column has been removed here as it is the yaw angle, γ, for all.
apply in larger arrays. Both optimization techniques3,36,102,103,111,114,119,120,128,137,140,145,148–150,157,158,161,163–165 and trial and error comparisons108,123,164 of different arrangements of yaw misalignment angles have been tested and have arrived at different conclusions. Several studies
have found that total power is optimized when the upstream turbines are misaligned the most and the misalignment angle is decreased as turbines
are located farther downstream.3,108,123,146,164 Gebraad et al136 used a game theoretic approach and found that a 3 2 wind farm was optimized
when the front row had misalignment angles of about 25 , the second row about 40 , and the back row was not misaligned as usual. Fleming
et al128 used a sequential quadratic programming method to optimize the Princess Amelia Wind Park. The angles are not given, but what can be
surmised from their figure is that, in a direction with the most turbines aligned with the wind, the optimization yaws the most upstream one or two
turbines of each column positively and all the rest in a column negatively, except for the last one, which is not misaligned.
Beyond determining the optimal yaw angles for each turbine, it is also necessary to consider implementation of the control. Yaw controls are
already designed to avoid what is known as “yaw hunting” as a result of trying to follow the time-varying wind direction at too high a frequency.
Similarly, with wake steering the goal is to balance yawing frequently enough to maintain power maximization while avoiding overuse of the
yawing components. One approach to implementing an optimization in a wind farm is to simulate all (or a subset of) the possible conditions and
create a lookup table of optimal yaw angles.112,121,130,132,133,145,150 Campagnolo et al112 demonstrated the need for good models when generating these tables, and Howland et al142 showed the strong dependence on atmospheric conditions. The tables can be programmed into the existing
yaw controller as an offset addition to the usual control signal without updating the logic, though there may still be issues when interfacing with a
proprietary controller.132,133 Doekemeijer et al,122 Simley et al,161 and Kanev145 all take different approaches to designing a controller that
accounts for wind variability while optimizing yaw angles. Doekemeijer et al122 reported a 1.4% increase in energy yield relative to greedy control
during their simulation, Simley et al161 reported an increase from 1.42% of wake losses recovered using a static lookup table approach to 3.24%
by accounting for the variability in wind direction, and Kanev145 reported an energy gain of 2.19% over the greedy control case when accounting
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
206
for the variability. Howland et al141 developed a closed-loop controller using data-driven estimates to determine optimal yaw misalignment angles
in real-time. Dhiman et al119 and Raach et al158 both modeled downstream pointing light detection and ranging (LiDAR) instruments to estimate
the wake centers of the turbines they are mounted on and determine the optimal yaw through wake models. Finally, Schreiber et al159 and
Howland et al140 both used supervisory control and data acquisition (SCADA) data to tune and validate models for optimal wake steering.
Best practices for controls implementation, both at the turbine and the wind farm level, still require additional research. At the turbine level,
original equipment manufacturers (OEMs) use proprietary control algorithms and researchers testing new designs must interface with those with
limited knowledge of the OEM's control algorithms. At the wind farm level, many questions remain regarding the optimal distribution of yaw offsets and how they are affected by various wind conditions and how to balance variability of wind conditions with excessive yawing.
3.2.2
|
Power maximization
Yawing a turbine to intentionally misalign it with the wind affects its performance. In terms of power production, a turbine that is not aligned with
the inflow cannot produce as much power both because its effective rotor-swept area is reduced and because of the effects of non-optimal
angles of attack for blades operating in a sheared inflow.118,163 Most of the literature on wake steering agrees about what happens to the turbines
downstream of misaligned turbines, especially in terms of power production. The potential for increased power production is highest when turbines are aligned with the inflow36,103,150 and have smaller downstream spacing between them,49,119,130,161 and the ambient turbulence levels are
low.40,132,133,161,164 These are the conditions for the largest gains because the baseline of a wind farm with these conditions produces less power
than offset turbines with large spacing and high TI. At least for a single column, the increase in power due to wake steering appears to increase
asymptotically for additional rows of aligned turbines.164 A transverse offset in turbine spacing of around half a diameter can yield a higher total
power,47,113,131,163 though offsets of a diameter or greater may cause the power benefits of wake steering to be negligible as downstream turbines are significantly less shadowed by upstream wakes.164
There is near universal agreement that positive yaw angles as defined above increase the total power of the array more than negative yaw
angles. In fact, many studies have independently arrived at an optimized yaw angle between 20 and 30 for the upstream turbine(s) when optimizing for total power,26,33,104,108,113,114,115,125,131,135,155,164 though it should be noted that actual wake deflection angles are usually much
smaller than the misalignment angle.153 Different theories have been offered as to why the direction of yaw matters, but the most advanced
explanation comes from Fleming et al,131 who used LES to explore the differences. Yawing a turbine creates two counter-rotating vortices, at the
top and bottom of the rotor, similar to an elliptical lifting line.160 When a turbine is positively yawed, the top vortex rotates the same direction as
the wake itself (opposite the rotor rotation), which strengthens that vortex. When negatively yawed, the lower vortex is enhanced in the same
way, but it also experiences lower wind speeds and ground shear. The top vortex, however, is unencumbered by the ground and in higher wind
speeds, which allows it to have a greater effect on the shape of the wake when the turbine is positively yawed. This vortex interaction turns the
elliptically-shaped wake of a yawed turbine into a sort of kidney shape that moves more of the wake out of alignment with downstream turbines.131 As the wake is advected downstream, the secondary flow produced by the upper vortex can continue to steer the flow of downstream
wakes in a process called “secondary steering.”4,131 Secondary steering is an extension of the effects of wake steering farther downstream due to
the altered flow patterns in the wake, and Zong and Porté-Agel164 showed that this is precisely why smaller yaw misalignments are needed for
turbines farther downstream. Some have even found that negative yaw misalignments decrease the total power of the array26,126,136,155 or that
use of negative misalignments can be limited while maintaining gains similar to those found when using both positive and negative
misalignments.128
While it is inherently different from wake steering, some have found that misaligning a downstream turbine that is waked by an upstream turbine can have a positive effect on the downstream turbine's power production106,154 and that conventional yaw controllers may even do this
automatically.154 This is likely due to the horizontal shear experienced by a waked turbine such that, by yawing, it accesses higher velocity flow
on one side of the rotor. Yawing the downstream turbine can also reduce the yaw moment on it.106
3.2.3
|
Effects on loads
The issue of loads when evaluating wake steering may be more complicated than power maximization. Yawing a turbine out of alignment with
the wind is an off-design strategy that is likely to, but may not always, increase the loading on it. Likewise, while power gains may be achieved for
nearly any reduction in wake overlap with a downstream turbine, the amount of overlap can significantly affect its loading. Beginning with the
intentionally misaligned turbine, some of its loads are reduced by virtue of producing less power. Low-speed shaft torque loads will decrease with
any misalignment, and the combined tower moment will decrease with positive yaw because of the decreased thrust force.163 This asymmetry
alludes to the complex fluid dynamics of yawing a rotating turbine in a sheared inflow. Assuming a turbine rotates clockwise looking downstream,
the effects of shear can be canceled by the rotation when it is yawed positively, whereas, in negative yaw, they are exacerbated.113,114,124 This is
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
207
HOUCK
HOUCK
also why the out-of-plane (OOP) blade root bending moment will decrease with positive yaw113,118,125,126,165 and the flapwise bending moment
and DEL as well.118,124 The edgewise bending moment and DEL increase with positive yaw and decrease with negative yaw.118,124 Kragh and
Hansen147 used analytical methods to calculate the ideal yaw angle to reduce the OOP blade root bending moment as a function of the shear
exponent and wind speed. Above a shear exponent of 0.2, the ideal yaw angle is only wind speed dependent and increases for higher speeds.
Beyond the blades, the drivetrain torsion and yaw bearing moments are frequently evaluated on the misaligned turbine. Fleming et al126
showed that these progressively decrease with positive misalignment angles. Zalkind and Pao163 further showed that the low-speed shaft torque
and bending moment have opposite trends with the former decreasing and the latter increasing for all yaws. Kragh and Hansen147 showed that
increasing turbulence decreases the yaw angle necessary to minimize the DELs of the OOP and in-plane blade root, tilt, and yaw moments.
Gebraad et al136 evaluated the loads on a 3 2 wind farm and showed that load reductions are dependent on the wind direction, which affects
the baseline wake overlap. Churchfield et al113 demonstrated that reductions in loads due to yawing were reduced as the wind direction shifted
away from being aligned with the turbines.
Considering downstream turbines that are not misaligned, Herges et al138 found that most of the fatigue loading could be spectrally associated with turbulence in fully waked conditions. In terms of fatigue loading only, a full wake is preferred over a partial wake, and several point out
that yawing cannot reduce loading due to ambient turbulence.118,138,147 In their field test, Herges et al138 found that fatigue loads in the flap and
edge directions of the blades increased as the upstream wake was shifted away from the downstream turbine center. The blade-root edge
moments continued to increase until the center of the steered wake was displaced by a little over a diameter from the center of the downstream
turbine and neither the blade root edge nor flap moments returned to unwaked levels until the wake center was over 1.5D away from full overlap.
Fleming et al126 showed that the OOP blade root moment of the downstream turbine peaked at the upstream misalignment angle that maximized
power between the two turbines. In contrast to the upstream, misaligned turbine, the drivetrain torsion and yaw bearing loads of the downstream
turbine increase with increasing upstream yaw125. As the upstream yaw continued to increase, however, the blade moment and yaw bearing loads
decreased while the drivetrain torsion continued to increase. Gebraad et al136 found that almost all DELs evaluated were reduced on almost all
turbines for the three wind directions for which yaw angles were optimized for power. Cases in which loads increased were attributed to partial
wake overlap.
Finally, in contrast to AIC in which power can be maximized and loads minimized in one strategy, with wake steering, the two may need
to be balanced. By giving an equal weighting to the increased power and the flapwise fatigue damage as a function of wake overlap, Herges
et al138 demonstrated that it is always beneficial to yaw an upstream wake away from full overlap. Similarly, van Dijk et al120 used an optimization procedure with different weightings for power maximization and load reduction, specifically the flapwise and edgewise moments. Optimizing yaw angles in a 3 3 wind farm over all wind directions with a constant velocity, the power could be improved 2.85% and the flap
and edgewise blade moments reduced 8.17% and 12.48%, respectively, with no weighting on loads. With a 30% weighting on loads, the
pez et al152 found a similar dependence on the weighting
moments were reduced over 40% while power was still increased 1.53%. Lo
between power and loads. Lin and Porté-Agel151 used wind tunnel and LES data to find the Pareto-optimal strategies for maximizing the
power and minimizing the loads of three aligned turbines. Their results suggest optimal strategies with misalignments of 10 –20 for the
upstream turbine and 0 –10 for the second one. Similar to what was shown above for AIC, Kanev et al3 optimized several wind farms for
lifetime power using their respective wind data such that increases in power were balanced by the effects of increases in loads on the wind
farm's lifetime. By optimizing a yawing strategy with the maximum yaw upstream linearly reduced across all rows of turbines to the minimum
yaw in the penultimate downstream turbine of each column for all wind directions, the lifetime average and worst-case loads were reduced
up to 30%. Load reductions were greatest in the blades and smallest in the towers. Under these optimizations, lifetime power was increased
as much as 1.9% and the lifetime of the wind farms increased as much as 0.9%. In another evaluation of the lifetime effects of wake steering,
Kanev et al146 simulated a wind farm over a complete range of conditions and found that virtually all fatigue loads were reduced across the
lifetime of the wind farm using wake steering. Ultimately, Kanev has shown that, although wake steering does increase some loads, it reduces
turbulence levels at downstream rotors. Since turbulence is the primary cause of fatigue, over a turbine's lifetime, most loads were decreased.
Finally, all else being equal, these trade-offs will be dependent on turbine size as well. Power increases with the square of the rotor diameter,
but fluctuating loads may be reduced because larger rotors have greater inertia to damp out smaller variations in their loading114 and other
components may work similarly.
3.2.4
|
Ancillary services
To the author's knowledge, only one study has explicity explored the use of wake steering to provide an ancillary service. Kretschmer et al148
showed that wake steering could be used to provide a minute-scale increase in power production under certain conditions. They simulated a
60-MW wind farm of 12 turbines and found it could provide at least an additional 1 MW of power by steering wakes when the wind was aligned
with multiple turbines in their 3 4 array. This idea essentially uses the waked condition as a power curtailment that holds additional power in
reserve.
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
208
Bastankhah and Porté-Agel108 found increased homogeneity of power in the five model turbines they optimized for power with wake
steering, and, similarly, Howland et al140 demonstrated reduced intermittency of power production while six full-scale turbines were operating
with optimized yaw misalignments. In both cases, it is likely that yawing upstream turbines allowed downstream turbines to operate more frequently at rated power by reducing periods of below rated wind speeds due to wakes. While this is not an active service, per se, reducing the variability of power production from wind will make it easier to integrate and operate with other base load generators on the grid.
3.2.5
|
Summary
In part due to the higher quality of research including the use of high-fidelity simulations and more field tests, wake steering by yaw control
appears to hold more promise for power maximization than AIC. There is also greater agreement about best practices for optimization and implementation, though wake steering also requires foreknowledge or online models. Since wake steering has advanced to the point of multiple field
tests, it now runs up against the difficulty of interfacing with proprietary controllers. Similar to AIC, the few studies that have attempted to calculate a true change in AEP including realistic wind data find that increases are in the low single digit percents.105,139,150,156 In contrast to AIC,
reducing the loads on turbines may be at odds with power maximization when using wake steering, though the reductions in loads that wake
steering can offer may be in the tens of percents rather than only single percents as estimated for AIC. Considering a lifetime of varying conditions, wake steering may reduce loads overall. Finally, it remains unclear if wake steering can be used for ancillary services on its own.
3.3
Combined AIC and wake steering
|
A third approach is to combine AIC with wake steering to maximize the benefits of each. As both still require further research to determine best
practices, few have attempted to implement them at the same time. A summary of studies of combined AIC and wake steering is in Table 3.
3.3.1
|
Control methods
It appears that no one has pursued implementation of yaw and induction control simultaneously in a field experiment. As successful wake steering
has been implemented, the larger question lies in how to best implement the induction control (see Section 3.1.1). Just as both of those methods
require previous knowledge or online models to determine optimal setpoints, using both together would only make this more complicated. Optimization procedures have been done, but only ones that could populate a lookup table and not work in real-time.78,168,169
3.3.2
|
Power maximization
Bossanyi166 used a mix of RANS CFD, models, and databases to optimize for power maximization of six aligned turbines. Optimizing over a three
hour period that included changes in wind direction, speed, and turbulence, he achieved power gains of about 2%. Jimenez et al144 demonstrated
TABLE 3
Summary of studies of axial induction control and wake steering together
Reference
Bossanyi
166
Castillo et al
167
Cossu115
Fleming et al
9
Jimenez et al144
Park et al
168
Park and Law78
Park et al
169
Focus
Method
Control parameter(s)
PM, LE
CFD, wake model
power
Wake
Wind tunnel
ω
PM
CFD
β
AS
CFD
β, K
Wake
CFD, wake model
Ct
PM
Wake model
β
PM
Wake model
β
PM
Wind tunnel
β
Note: The focus of the study is broadly defined as power maximization (PM), load effects (LE), and/or ancillary services (AS), or the study may have focused
on the wake of the controlled turbine (wake). The method refers to how the wind turbine wake was generated in the study (wake model, field test, etc.).
The control parameter is the variable that was used in the study to derate the turbine(s) other than the yaw angle, γ.
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
209
HOUCK
HOUCK
with both LES and an analytical model that increasing a turbine's thrust while misaligning it also increased the centerline deflection of its wake,
and Castillo et al167 confirmed this in a wind tunnel. The increase in thrust is also likely to increase the wake spread rate due to the added turbulence, but it is unclear if this washes out the effects of the increased deflection. Similarly, Cossu115 used SOWFA170 to explicitly investigate the
effect of combined overinduction and wake steering and also found that it increased the deviation of the controlled turbine's wake away from
downstream turbines. In a wind farm with two spanwise-periodic rows of seven turbines each, he found a more than 15% increase in total farm
power when the front row was misaligned by 30 and the pitch angle was reduced to 4 to achieve a higher thrust. These results suggest an
opportunity to further optimize the wake characteristics of an intentionally misaligned turbine.
The most work on optimizing induction and yaw together has come from Park and Law.78 In their most advanced work, they used a cooperative game approach to optimize the induction and yaw for power maximization in a 5 5 wind farm spaced 7D 5D and calculated results in all
directions at a constant wind speed. Their results indicated that individual turbine control may be the combination of the optimal approaches generally seen for each control method individually. That is, induction factors were reduced on upstream turbines, and they also steered their wakes
away from downstream turbines. Wind farm efficiency improvements with this strategy reached nearly 40% for the wind direction with the
smallest aligned spacing. Maintaining the previous spacing, they extended the optimization to larger and larger square arrays of turbines in
the three wind directions that yielded full wake conditions without optimization. They found that the optimized power increases with the number
of turbines and especially for the direction with the smallest effective spacing, though this improvement appears to be asymptotic. Finally, they
ran the optimization for all directions with a constant wind speed using the layout of the Horns Rev wind farm. Including the probability distribution for wind directions, they estimate an annual power increase of 7.14%. This, however, does not include a distribution of wind speeds or the
effects of turbulence, both of which are likely to reduce gains.
3.3.3
|
Effects on loads
Only Bossanyi166 has estimated load reductions while using both induction and yaw controls. Repeating the simulation mentioned just above, load
reduction was given a 10% weighting relative to increased power production to optimize the setpoints and yaw angles. Power production was
increased 2.06%, while blade and tower loads were decreased 2.86% and 4.16%, respectively, though uncertainty is admitted to be high. Based
on other studies of AIC and wake steering separately, it is likely that the benefits to loads are additive, though mostly contributed by wake
steering.
3.3.4
|
Ancillary services
The only available study on using yaw and induction controls together for ancillary services comes from Fleming et al,9 who used them to design
a controller to follow a reference signal with 80% or 90% power reserves. Testing it on a 3 3 wind farm with a high-fidelity CFD method, they
found that adding wake steering to pitch control improved the reference following ability of the third row due to the reduction in waked states,
but worsened that of the second row due to its extreme misalignment. Its reference following ability fell somewhere between using just pitch or
torque control, which also fell far short of following the signal, but the mean power from using pitch and yaw controls together was higher than
any other method. It also managed this while achieving similar if not greater load reductions than other methods.
3.3.5
|
Summary
From the little research available on the combination of yaw and induction controls, it appears that both the advantages and disadvantages of
each are unchanged when combined. Specifically, they still require prior optimizations or real-time computing to implement in the field and the
best practices for induction control (pitch or torque) are still uncertain. Using AIC and wake steering together may offer even greater reductions
in loads and further flexibility in providing ancillary services. The combined use of these two strategies may be best suited for conditions during
which partial wake overlap is unavoidable. When wake steering is insufficient to steer a wake completely away from a downstream turbine,
derating the upstream, misaligned turbine may provide the optimal balance of power increase and load reduction.
3.4
|
Active wake control
While AWC (also known as dynamic induction control (DIC)) manipulates a turbine's induction factor in an effort to mitigate its wake, the similarities to the similarly named AIC end there. Rather than derate a turbine to a static setpoint, AWC involves constantly changing the operating point
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
210
of a turbine in a strategic way that causes its wake to dissipate faster. Again, the reference to induction is misleading, and moreso here than with
AIC, as any dynamic wake manipulation, including yawing, may be considered for this technique. This is why the term active wake control is preferred over dynamic induction control. This is the newest wake management technique being investigated with all experiments taking place in the
last 5 years.
3.4.1
|
Control methods
There has been very little work exploring ways to implement AWC in full-scale turbines. Brown et al171 argue that yaw mechanisms are too slow
to achieve meaningful AWC. While studies of active yawing have been done (see Table 4), most studies have not used yaw, while torque and
especially pitch control are commonly used for AWC. Using the same methods as Goit et al172 and Munters and Meyers,98,99 Yilmaz
and Meyers173 developed a simplified control signal for an upstream turbine and tested it with one downstream turbine at 3D or 5D, three different turbulence levels (though without shear), and four different integral length scales. In their optimization, they derived periodic signals for both
the torque and the pitch to implement AWC using the upstream turbine. These controls created a periodic fluctuation in the turbine's rotation
rate achieved by using the rotational inertia in the rotor. This allowed the controlled turbine to, at times, exceed the power output of its MPPT
control. Overall, they found that lower turbulence levels and integral length scales resulted in greater total power.
In wind tunnel56 and LES175 experiments, Frederik et al. used pitch control to implement AWC. In an interesting extension of the technique,
they also used dynamic individual pitch control (DIPC), which allowed them to steer the wake up and down, left and right, and, most effectively,
in a helix, though this steering is a much smaller magnitude than wake steering using yaw control. In the wind tunnel test, they found that the
added signal for AWC did not interfere with the turbine's trimmer performance as it adjusted to the inflow. In this test, they searched around
the optimal frequency identified by Munters and Meyers99, a diameter-based Strouhal number of 0.25, and found a slightly higher frequency
(St = 0.32) was optimal along with the lowest of the amplitudes (with respect to thrust) tested in both a high and a low turbulence case. They also
concluded that the amplitude was more important than the frequency, at least with higher turbulence, though they did not explore the physical
reasons why.56 In the LES study,175 their search led them to the same optimal frequency found by Munters and Meyers.99 The amplitude was
tested at only two levels, and, rather than in terms of thrust, it was tested with respect to the pitch rate. The higher pitch rate produced at least
twice the energy in the wake of the controlled turbine or twice the total power when there were two turbines for all methods and cases tested.
Using nearly the same frequency and amplitude, Wang et al180 saw a 3% increase in the total power of three aligned turbines.
It is possible that the optimal amplitude and period for AWC implementation are related to the instabilities that naturally occur in the turbine
wake and that aid in its decay. Identifying, measuring, and even manipulating these instabilities have been the subject of many
TABLE 4
Summary of studies of dynamic induction control
Reference
Focus
Method
Control parameter(s)
Wake
CFD
β, ω
PM
CFD
β
LE
CFD
β
PM
CFD
β
PM, LE
BEM, wind tunnel
β
172
PM
CFD
Ct
Houck and Cowen28
PM
Flume
TSR
Wake, LE
CFD
γ
Wake
CFD
Blade flaps
97
PM
CFD
Ct
Munters and Meyers98
PM
CFD
Ct
99
Brown et al
171
Cacciola et al
174
Croce et al116
Frederik et al
175
Frederik et al56
Goit et al
Kimura et al
176
Marten et al177
Munters and Meyers
PM
CFD
Ct, γ
Munters and Meyers178
PM
CFD
Ct, γ
179
PM
CFD
Ct
PM, LE
CFD, wind tunnel
β
PM
CFD
β, K
Munters and Meyers
Munters and Meyers
Wang et al180
Yılmaz and Meyers
173
Note: The focus of the study is broadly defined as power maximization (PM), load effects (LE), and/or ancillary services (AS). The method refers to how the
wind turbine wake was generated in the study (wake model, field test, etc.). The control parameter is the variable that was used in the study to derate the
turbine(s) or steer its wake.
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
211
HOUCK
HOUCK
studies.28–30,100,181–187 Brown et al171 identify two possible optimal forcing strategies from previous literature: forcing the bluff-body instability
or forcing the mutual induction (i.e., tip vortex) instability. While most research has focused on triggering the former,56,99,175,180 Brown et al171
confirmed previous work30,177 suggesting that a frequency equal to one-and-a-half times the rotation rate was optimal for accelerating tip vortex
instability and was able to decrease the near-wake length (defined by the end of the linear growth region of the instability) by as much as 80%
using periodic pitching, rotor speed, or both in tandem. The optimal frequency and/or amplitude for AWC is likely dependent on the turbine and
the atmospheric conditions and other parameters such as wave form and duty cycle have yet to be explored. The physical mechanisms responsible for enhanced wake decay have also not been explored in depth. Additional work is required and the technique may need to be optimized for
each turbine implementing it and perhaps even for the atmospheric conditions.
3.4.2
|
Power maximization
As mentioned previously, Goit et al172 were the first to suggest AWC and have remained at the forefront of research regarding it. Their initial
approach was an optimization technique that allowed the thrust of every wind turbine in an array to be optimized for total array power continually throughout the simulation. Using this optimization, they found that AWC produced the greatest total power in a staggered wind farm with relatively wide spacing but provided the largest gain compared to MPPT in an aligned wind farm with relatively tight spacing. This can be attributed
mostly to the difference in the baseline for the two farm configurations. They also found that overinduction (increasing the thrust beyond its optimum) worked better than underinduction.97,99
Since then, Munters and Meyers have explored their own results and developed a reduced operation strategy that optimizes the thrust of
only the first row of turbines, and further simplified it to use a set amplitude and period based on a parameter search.179 Testing this optimization
for the front row of a 4 4 wind farm, they achieved wind farm efficiency increases of a few percent and found the optimized period and amplitude to be robust to both turbine spacing and turbulence levels. Proceeding to a 12 6 wind farm, they found that power gains were restricted
to the second and third rows of turbines and were never greater than 0.5%. They showed that the fluid dynamic mechanism at work is the periodic shedding of vortex rings by the upstream turbines using AWC (it should be noted that they use a non-rotating actuator disc), which accelerates energy entrainment from the top of the internal boundary layer created by the wind farm. It does not, however, increase total entrainment
evidenced by the fact that rows farther downstream had reduced wind speeds compared to the reference case. Their results suggest that it may
be advantageous to operate one row of turbines with AWC every few rows to renew its positive effects.
To identify optimal frequecies, Houck and Cowen28 used the rescaled characteristic frequencies of instabilities identified in previous studies
as the frequencies at which to oscillate the setpoint of a model turbine while measuring its wake. Contrary to expectations, they found that the
wake decay rate was invariant to the frequency used but depended strongly on the amplitude, which was equated with the TSRs used with a constant inflow velocity. When oscillating the setpoint to a higher TSR than optimal, the wakes decayed faster. They theorized that the higher TSRs
reduced the pitch of the helical tip vortices, which promoted their interactions and accelerated their destruction and the wake's mixing with the
ambient flow. This may correspond to previous findings that overinduction, which also increases the rotation rate, creates larger power gains than
underinduction.97,99
Most recently, Frederik et al175 demonstrated a 7.5% total power increase by steering the upstream wake in a counterclockwise helix using
DIPC, while simpler impementation of AWC with collective pitch control achieved a 4.6% increase in their LES. These results were both from a
two-turbine case with turbulent inflow. In the wind tunnel test with three aligned wind turbines using collective pitch AWC, Frederik et al56 found
a 2.4% increase in total power with 5% TI and a 4% increase in total power with 10% TI. In both cases, the increases are almost entirely in the second turbine, which is further evidence of the short-lived effects of AWC. An AEP analysis suggests that the controlled turbine's power would only
be reduced about a 0.5%, and that is if it used AWC all the time.
Munters and Meyers99,178 explore a number of dynamic yaw techniques, some of which are combined with AWC using pitch and torque control. The highest farm efficiency from their simulated 4 4 wind farm with turbines and wind aligned and a turbulent inflow comes from dynamic
yawing with dynamic overinduction. They further find that, in a wide and staggered farm, this approach can achieve 84% wind farm efficiency.
Recall, though, that this approach requires continual updates to the turbine controls based on complete knowledge of the flow and other turbines.
Kimura et al176 showed that wake decay is accelerated during dynamic yawing due to the forced interaction of tip and hub vortices.
3.4.3
|
Effects on loads
Estimates of loading effects from dynamic pitching have been made by Croce et al116 Frederik et al56 and Wang et al.180 The former two used the
aeroelastic tool Cp-Lambda188 to estimate DELs on the controlled turbine, but not a waked turbine. Croce et al116 found that both the tower base
fore-aft moment and blade root flapwise moment increased with increasing Strouhal and pitching amplitude. Frederik et al56 found that, using a
pitch amplitude of 2 , the tower base fore-aft moment was affected the most, up to 11% higher, and the blade flapwise root load increased up to
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
212
2%. These are worst-case results, though, because the analysis was done as if AWC was operating at all times, even above rated wind speeds. In
contrast, Wang et al180 used the program Fatigue, Aerodynamics, Structures, and Turbulence Modeling (FAST)189 and SOWFA170 on all three simulated turbines. They found that the upstream, actuated turbine had blade root and tower DEL increases of 106% and 216%, respectively. For
those same quantities, the second turbine had more modest increases of 16% and 65%, respsectively, and the third turbine increases of 3% and
11%, respectively. For dynamic yawing, Kimura et al176 found that both the minimum and maximum OOP moments on the turbine increased
exponentially as the amplitude of the yaw was increased linearly.
We can further speculate that, given the periodic nature of the control strategy, there is cause for concern. Many turbine loads are closely
coupled to thrust, which is in turn coupled to all turbine controls, so there is likely no avoiding periodic increases in loading and additional fatigue
while implementing a dynamic controller. The helix approach from Frederik et al175 may trade smaller magnitudes of thrust variation for changes
in the direction of the thrust vector. Furthermore, it is likely critical to avoid resonance with the existing bending modes of the turbine tower and
blades. Additionally, though turbines are constantly responding to wind conditions through their controllers, periodic actuation of the controllers
may lead to increased fatigue loading and accelerated wear of associated components. Because implementation of AWC may only involve the
most upstream turbines in an array, its use may require an economic analysis to evaluate the trade-offs between increased power production,
increased maintenance, and decreased lifetime.
3.4.4
|
Ancillary services
The potential for AWC to be used to provide ancillary services has not been evaluated. Because AWC may only involve a change in operation of
upstream turbines, it does not preclude the possibility of changing the operation of downstream turbines to optimize the wind farm for ancillary services. It remains possible that such a strategy would allow a wind farm to better maintain typical power production levels while providing additional
services due to the accelerated wake recovery of the upstream turbines. Finally, the work of Munter and Meyers98 indicates that AWC may create
fluctuations in power production larger and more frequent than those using MPPT. Their study involved the continual control of all turbines in the
array, though the two turbine study of Yilmaz and Meyers173 using a constant control signal in only the upstream turbine showed a similar increase in
fluctuations. This could mean that rather than any potential to provide ancillary services, AWC may create a requirement for additional grid balancing.
3.4.5
|
Summary
AWC requires a great deal more research before it can be implemented in a full-scale wind farm, in particular due to concerns for the additional
loads it causes in what may be a relatively extreme operation strategy. Testing AWC on a single full-scale turbine could easily address many of
the remaining questions including its effects on turbine loads and how to implement full-scale controls. Because of the risk involved, it is likely to
require high-fidelity aeroelastic simulations to determine safe implementation. These simulations would also be useful in further identifying the
optimal periods and amplitudes and any dependencies they have on the turbine and/or the atmospheric conditions.
4
|
S U M M A R Y A N D CO N C L U S I O N S
The early years of wind energy's technology advancement were largely focused on the individual turbine, and it is only more recently that
research on their operation in arrays has become a major focus of this field. There is still much to learn regarding best practices for the often competing objectives of power maximization, load reduction, and ancillary services. Furthermore, wind turbines' inherent operation in an environment
rife with interdependent and time-varying characteristics makes optimization on any front an elusive goal. Nonetheless, advancements in computing power, sensors and measurement techniques, and general understanding have allowed researchers to develop several promising wake management methods to improve the performance of wind farms.
While the operating environment for wind turbines represents an enormous parameter space, the design space for wake management techniques is limited. Turbines have three primary control actuators: pitch, torque, and yaw. From these, four techniques have emerged as most promising. Currently, wake steering appears most advanced, though its benefits are not as clear cut as those of AIC (when it shows a benefit)
considering the former often involves trade-offs in power increases and load reductions while the latter likely does not. AIC shows potential but
still requires more research than wake steering. The combination of these two may yet prove to be the real winner among wake management
techniques as their advantages and disadvantages are so often complementary. Finally, AWC has the shine of a big new idea with great potential
but may be impossible to actually implement at full scale. It remains to be seen whether or not its costs will outweigh its benefits.
With regard to future research, the following general recommendations are given. All methods will benefit from an open-source controller
software and hardware to provide a common baseline for comparison and reduce the need to interface with proprietary controllers. AIC requires
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
213
HOUCK
HOUCK
research targeted at optimal control methods that can be replicated at full scale and optimized for arrays. Low-fidelity simulations will not suffice
for this unless they have been updated to include the effects of thrust and rotor-added turbulence. Wake steering is advanced enough to focus
more on optimization methods, particularly with regard to large arrays and atmospheric conditions. While there have been several full-scale tests
of wake steering, working with proprietary yaw controllers has proven challenging and greater collaboration between researchers and turbine
OEMs is required to better understand best practices for implementation. Lessons learned from AIC and wake steering are likely additive and will
point the way to next steps required for their combined operation. Finally, efforts pursuing AWC should focus on what is required to test it at full
scale. It is the author's hope that this review will aid in guiding future research to address the most pressing and outstanding questions in the field.
ACKNOWLEDGEMEN TS
Funding is provided by US Department of Energy, Wind Energy Technology Office, in the A2e research portfolio, in the Rotor Wake project. This
paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily
represent the views of the U.S. Department of Energy or the United States Government. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
NOMENCLATURE
a
axial induction factor
AEP
annual energy production
AIC
axial induction control
AGC
automatic generation control
APC
active power control
AWC
active wake control
BEM
blade element momentum
CFD
computational fluid dynamics
CP
coefficient of power
Ct
coefficient of thrust
D
rotor diameter
DEL
damage equivalent load
DIC
dynamic induction control
DIPC
dynamic individual pitch control
K
generator torque
LCoE
levelized cost of energy
LES
large eddy simulation
LiDAR
light detection and ranging
MPPT
maximum power point tracking
OEM
original equipment manufacturer
OOP
out of plane
RANS
Reynolds-averaged Navier–Stokes
SCADA
supervisory control and data acquisition
TI
turbulence intensity
TSR
tip speed ratio
U
U
streamwise velocity
Udisc
streamwise velocity at the rotor disc
U∞
freestream streamwise velocity
uwake
streamwise velocity in the wake
VD
velocity deficit
β
pitch angle
γ
yaw angle
σU
standard deviation of streamwise velocity
ω
rotor rotation rate
temporal average of streamwise velocity
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
214
P EE R R EV I E W
The peer review history for this article is available at https://publons.com/publon/10.1002/we.2668.
DATA AVAI LAB ILITY S TATEMENT
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
ORCID
Daniel R. Houck
https://orcid.org/0000-0002-3700-7543
RE FE R ENC E S
1. Global Wind Energy Council. Global Wind Report 2019; 2019.
2. Kheirabadi AC, Nagamune R. A quantitative review of wind farm control with the objective of wind farm power maximization. J Wind Eng Ind
Aerodyn. 2019;192:45-73. https://doi.org/10.1016/j.renene.2011.01.024
3. Kanev S, Savenije FJ, Engels W. Active wake control: An approach to optimize the lifetime operation of wind farms. Wind Energy. 2018;21:488-501.
https://doi.org/10.1002/we.2173
4. King J, Fleming P, King R, Martínez-Tossas L, Bay C, Mudafort R, Simley E. Controls-oriented model for secondary effects of wake steering. In: Wind
Energy Science Discussions, 2020:1-22. https://doi.org/10.5194/wes-2020-3
5. Barthelmie RJ, Rathmann O, Frandsen ST, et al. Modelling and measurements of wakes in large wind farms. J Phys: Conf Ser. 2007;75:12049. https://
doi.org/10.1088/1742-6596/75/1/012049
6. Barthelmie RJ, Hansen KS, Pryor SC. Meteorological controls on wind turbine wakes. Proc IEEE. 2013;101(4):1010-1019. https://doi.org/10.1109/
JPROC.2012.2204029
7. Vali M, Petrovic V, Steinfeld G, Y Pao L, Kühn M. An active power control approach for wake-induced load alleviation in a fully developed wind farm
boundary layer. Wind Energy Sci. 2019;4(1):139-161. https://doi.org/10.5194/wes-4-139-2019
8. Denholm P, Sun Y, Mai T. An introduction to grid services: Concepts, technical requirements, and provision from wind. NREL/TP6A20-72578, National Renewable Energy Laboratory; 2019.
9. Fleming P, Aho J, Gebraad P, Pao L, Zhang Y. Computational fluid dynamics simulation study of active power control in wind plants. In: American
Control Conference; 2016:1413-1420. https://doi.org/10.1109/ACC.2016.7525115
10. Aho J, Buckspan A, Laks J, et al. A tutorial of wind turbine control for supporting grid frequency through active power control. In: Proceedings of the
American Control Conference. IEEE; 2012:3120-3131. https://doi.org/10.1109/acc.2012.6315180
11. Gravagne I, Guttromson R, Berg J, White J, Wilches-bernal F, Summers A, Harrel M. Use and testing of a wind turbine for the supply of balancing
reserves and wide-area grid stability Sandia National Laboratories. SAND2018-7730, Sandia National Laboratories; 2018.
12. Guttromson R, White J, Berg J, Wilches-bernal F, Hansen C, Paquette J, Gravagne I. Use of wind turbine kinetic energy to supply transmission level
services Sandia National Laboratories. SAND2018-772151, Sandia National Laboratories; 2018.
13. Lee J, Muljadi E, Sørensen P, Kang YC. Releasable kinetic energy-based inertial control of a DFIG wind power plant. IEEE Trans Sustain Energy. 2016;
7(1):279-288. https://doi.org/10.1109/TSTE.2015.2493165
14. Tian J, Zhou D, Su C, Chen Z, Blaabjerg F. Reactive power dispatch method in wind farms to improve the lifetime of power converter considering
wake effect. IEEE Trans Sustain Energy. 2017;8(2):477-487. https://doi.org/10.1109/TSTE.2016.2607146
15. Ullah NR, Bhattacharya K, Thiringer T. Wind farms as reactive power ancillary service providers—Technical and economic issues. IEEE Trans Energy
Convers. 2009;24(3):661-672. https://doi.org/10.1109/TEC.2008.2008957
16. Wang N, Li J, Yu X, et al. Optimal active and reactive power cooperative dispatch strategy of wind farm considering levelised production cost
minimisation. Renew Energy. 2019;148:113-123. https://doi.org/10.1016/j.renene.2019.12.022
17. Boersma S, Doekemeijer BM, Gebraad PMO, et al. A tutorial on control-oriented modeling and control of wind farms. In: 2017 American Control
Conference (ACC). AACC; 2017:1-18. https://doi.org/10.23919/ACC.2017.7962923
18. Andersson LE, Anaya-Lara O, Tande JO, Merz KO, Imsland L. Wind farm control—part I: A review on control system concepts and structures. IET
Renew Power Gen. 2021;15:2085-2108. https://doi.org/10.1049/rpg2.12160
19. Knudsen T, Bak T, Svenstrup M. Survey of wind farm control—power and fatigue optimization. Wind Energy. 2014;18(8):1333-1351. https://doi.org/
10.1002/we.1760
20. Porté-Agel F, Bastankhah M, Shamsoddin S. Wind-Turbine and Wind-Farm Flows: A Review, Vol. 174. Netherlands: Springer; 2019. https://doi.org/10.
1007/s10546-019-00473-0
21. Van Wingerden JW, Fleming PA, Göcmen T, et al. Expert elicitation on wind farm control. J Phys: Conf Ser. 2020;1618(2):22025. https://doi.org/10.
1088/1742-6596/1618/2/022025
22. Burton T, Sharpe D, Jenkins N, Bossanyi E. Wind Energy Handbook. 1st ed. West Sussex: John Wiley & Sons Ltd.; 2001.
23. Kelley CL. Aerodynamic design of the national rotor testbed. SAND2015-8989, Sandia National Laboratories; 2015.
24. Njiri JG, Söffker D. State-of-the-art in wind turbine control: Trends and challenges. Renew Sustain Energy Rev. 2016;60:377-393. https://doi.org/10.
1016/j.rser.2016.01.110
25. Pao LY, Johnson KE. A tutorial on the dynamics and control of wind turbines and wind farms. In: American Control Conference; 2009:2076-2089.
26. Gebraad PMO, Fleming PA, Van Wingerden JW. Comparison of actuation methods for wake control in wind plants. In: Proceedings of the American
Control Conference; 2015:1695-1701. https://doi.org/10.1109/ACC.2015.7170977
27. Gryning SE, Floors R, Peña A, Batchvarova E, Brümmer B. Weibull wind-speed distribution parameters derived from a combination of wind-lidar and
Tall–Mast measurements over land, coastal and marine sites. Bound-Layer Meteorol. 2016;159(2):329-348. https://doi.org/10.1007/s10546-0150113-x
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
215
HOUCK
HOUCK
28. Houck D, Cowen EA. Can you accelerate wind turbine wake decay with unsteady operation? In: AIAA SciTech 2019 Forum; 2019. https://doi.org/
10.2514/6.2019-2084
29. Iungo GV, Viola F, Camarri S, Porté-Agel F, Gallaire F. Linear stability analysis of wind turbine wakes performed on wind tunnel measurements.
J Fluid Mech. 2013;737:499-526. https://doi.org/10.1017/jfm.2013.569
30. Ivanell S, Mikkelsen R, Sorensen JN, Henningson D. Stability analysis of the tip vortices of a wind turbine. Wind Energy. 2010;13(March 2010):
705-715. https://doi.org/10.1002/we,_arXiv:arXiv:1006.4405v1
31. Lignarolo LEM, Ragni D, Scarano F, Sim~
ao Ferreira CJ, van Bussel GJW, Ferreira CJS. Tip-vortex instability and turbulent mixing in wind-turbine
wakes. J Fluid Mech. 2015;781:467-493. https://doi.org/10.1017/jfm.2015.470
32. Abbes M, Allagui M. Centralized control strategy for energy maximization of large array wind turbines. Sustain Cities Soc. 2016;25:82-89. https://doi.
org/10.1016/j.scs.2015.11.007
33. Adaramola MS, Krogstad P-A. Experimental investigation of wake effects on wind turbine performance. Renew Energy. 2011;36(8):2078-2086.
https://doi.org/10.1016/j.renene.2011.01.024
34. Ahmad T, Girard N, Kazemtabrizi B, Matthews PCPCPC, Sciences C. Analysis of two onshore wind farms with a dynamic farm controller. In: EWEA
2015, Vol. 44; 2015:5-9.
35. Ahmad T, Coupiac O, Petit A, Guignard S, Girard N, Matthews PC, Kazemtabrizi B. Field implementation and trial of coordinated control of Wind
farms. IEEE Trans Sustain Energy. 2018;9(3):1169-1176. https://doi.org/10.1109/TSTE.2017.2774508
36. Ahmad T, Basit A, Anwar J, Coupiac O, Kazemtabrizi B, Matthews PC. Fast processing intelligent wind farm controller for production maximisation.
Energies. 2019;12:1-17. https://doi.org/10.3390/en12030544
37. Annoni J, Gebraad PMO, Scholbrock AK, Fleming P, van Wingerden JW. Analysis of axial-induction-based wind plant control using an engineering
and a high-order wind plant model. Wind Energy. 2015;19(August 2015):1135-1150. https://doi.org/10.1002/we,_arXiv:arXiv:1006.4405v1
38. Barradas-Berglind JJ, Wisniewski R. Wind farm axial-induction factor optimization for power maximization and load alleviation. In: 2016 European
Control Conference (ECC). IEEE; 2016:891-896. https://doi.org/10.1109/ECC.2016.7810402
39. Bartl J, Sætran L. Experimental testing of axial induction based control strategies for wake control and wind farm optimization. In: Journal of Physics:
Conference Series, Vol. 753; 2016. https://doi.org/10.1088/1742-6596/753/3/032035
40. Bartl J. Experimental testing of wind turbine wake control methods. Ph.D. Thesis: Norwegian University of Science and Technology; 2018.
41. Behnood A, Gharavi H, Vahidi B, Riahy GH. Optimal output power of not properly designed wind farms, considering wake effects. Int J Electr Power
Energy Syst. 2014;63:44-50. https://doi.org/10.1016/j.ijepes.2014.05.052
42. Bitar E, Seiler P. Coordinated control of a wind turbine array for power maximization. In: 2013 American Control Conference (ACC); 2013:
2898-2904. https://doi.org/10.1109/ACC.2013.6580274
43. Boorsma K. Heat and flux analysis of field measurements. Technical Report. ECN-E–12-048; 2012.
44. Bossanyi E, Ruisi R. Axial induction controller field test at Sedini wind farm. Wind Energy Sci. 2021;6(2):389-408. https://doi.org/10.5194/wes-6389-2021
45. Brand A, Bot E, Kanev S, Savenije FJ, Ozdemir H. Wind farm design and active wake control. In: EWEA 2014; 2014.
46. Bubshait A, Alsaleem A, Simoes MG. Centralized power reserve algorithm of de-loaded wind farm for primary frequency regulation. In: 2018 IEEE
Energy Conversion Congress and Exposition, ECCE 2018; 2018:423-429. https://doi.org/10.1109/ECCE.2018.8557561
47. Campagnolo F, Petrovi V, Bottasso CL, Croce A. Wind tunnel testing of wake control strategies. In: American Control Conference 2016; 2016:1-6.
https://doi.org/10.1109/ACC.2016.7524965
48. Ceccotti C, Spiga A, Bartl J, Saetran L, Stran L. Effect of upstream turbine tip speed variations on downstream turbine performance. Energy Procedia.
2016;94(1876):478-486. https://doi.org/10.1016/j.egypro.2016.09.218
49. Chhor J, Matschke A, Kipke V, Sourkounis C. Operation and control strategies for wind energy conversion systems: Review and simulation study. In:
2019 14th International Conference on Ecological Vehicles and Renewable Energies, EVER 2019. IEEE; 2019:1-9. https://doi.org/10.1109/EVER.
2019.8813521
50. Ciri U, Rotea M, Santoni C, Leonardi S. Large-eddy simulations with extremum-seeking control for individual wind turbine power optimization. Wind
Energy. 2017;20(9):1617-1634. https://doi.org/10.1002/we.2112
51. Cole M, Stock A, Leithead WE, Amos L. Using rotor inertia as stored energy in below rated wind farms to provide primary frequency response. In:
Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022026
52. Corten GP, Schaak P. More power and less loads in wind farms: 'Heat and flux'. In: European Wind Energy Conference; 2004.
53. Dam FCV, Gebraad PMO, Wingerden J-WV. A maximum power point tracking approach for wind farm control. Journal of Physics: Conference Series;
2012.
54. De-Prada-Gil M, Alías CG, Gomis-Bellmunt O, Sumper A. Maximum wind power plant generation by reducing the wake effect. Energy Convers Manag.
2015;101:73-84. https://doi.org/10.1016/j.enconman.2015.05.035
55. Dilip D, Porté-Agel F. Wind turbine wake mitigation through blade pitch offset. Energies. 2017;10:757. https://doi.org/10.3390/en10060757
56. Frederik JA, Weber R, Cacciola S, Campagnolo F, Croce A, Bottasso C, van Wingerden J-W. Periodic dynamic induction control of wind farms: proving the potential in simulations and wind tunnel experiments. Wind Energy Sci. 2020;5(1):245-257. https://doi.org/10.5194/wes-5-245-2020
57. Galinos C, Larsen TJ, Mirzaei M. Impact on wind turbine loads from different down regulation control strategies. J Phys Conf Ser. 2018;1104(1):
12019. https://doi.org/10.1088/1742-6596/1104/1/012019
58. Gao W, Wang X, Muljadi E, Gevorgian V, Scholbrock A. Real-time digital simulation of inertial response with implementation on the CART3 wind turbine at the National Wind Technology Center; 2017.
59. González JS, Payán MB, Santos JR. Optimal control of wind turbines for minimizing overall wake effect losses in offshore wind farms. In: IEEE
EuroCon 2013;2013:1129-1134. https://doi.org/10.1109/EUROCON.2013.6625122
pez B, Usera G. Actuator line model simulations to study active power control at wind turbine level. In: Journal of Physics:
60. Guggeri A, Draper M, Lo
Conference Series, Vol. 1256; 2019. https://doi.org/10.1088/1742-6596/1256/1/012030
61. Herp J, Poulsen UV, Greiner M. Wind farm power optimization including flow variability. Renew Energy. 2015;81:173-181. https://doi.org/10.1016/j.
renene.2015.03.034
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
216
217
62. van der Hoek D, Kanev S, Allin J, et al. Effects of axial induction control on wind farm energy production—a field test. Renew Energy. 2019;140:
994-1003. https://doi.org/10.1016/j.renene.2019.03.117
63. Horvat T, Spudic V, Baotic M. Quasi-stationary optimal control for wind farm with closely spaced turbines. In: MIPRO, 2012 Proceedings of the 35th
International Convention. IEEE; 2012:829-834.
64. Johnson KE, Thomas N. Wind farm control: addressing the aerodynamic interaction among wind turbines. In: 2009 American Control Conference.
IEEE; 2009:2104-2109. https://doi.org/10.1109/ACC.2009.5160152
65. Johnson KE, Fritsch G. Assessment of extremum seeking control for wind farm energy production. Wind Eng. 2012;36(6):701-715. https://doi.org/
10.1260/0309-524X.36.6.701
66. Kayedpour N, Samani AE, Singh N, De JDM. An optimal control strategy to maximize power in an offshore wind farm by reducing wake interaction
axial induction nontrol. In: 16th EAWE PHD Seminar on Wind Energy; 2020.
67. Kazda J, Zendehbad M, Jafari S, Chokani N, Abhari RS. Mitigating adverse wake effects in a wind farm using non-optimum operational conditions.
J Wind Eng Ind Aerodyn. 2016;154:76-83. https://doi.org/10.1016/j.jweia.2016.04.004
68. Kiani A, Abdulrahman M, Wood D. Explicit solutions for simple models of wind turbine interference. Wind Eng. 2014;38(2):167-180. https://doi.org/
10.1260/0309-524X.38.2.167
69. Kim H, Kim K, Paek I, Bottasso CL, Campagnolo F. A study on the active induction control of upstream wind turbines for total power increases. In:
Journal of Physics: Conference Series, Vol. 753; 2016. https://doi.org/10.1088/1742-6596/753/3/032014
70. Kondo S, Inage S. Generated output increase control with consideration of the influence of wake for wind farm. Electr Eng Japan (English translation
of Denki Gakkai Ronbunshi). 2019;208(3-4):39-47. https://doi.org/10.1002/eej.23170
71. Kucuksahin D, Bot ETG. Heat and flux configurations on offshore wind farms. In: Journal of Physics: Conference Series, Vol. 555; 2014. https://doi.
org/10.1088/1742-6596/555/1/012061
72. Lee J, Son E, Hwang B, Lee S. Blade pitch angle control for aerodynamic performance optimization of a wind farm. Renew Energy. 2013;54:124-130.
https://doi.org/10.1016/j.renene.2012.08.048
73. Lyu X, Jia Y, Xu Z, Xu X. An active power regulation strategy for wind farm considering wake effect. In: 2019 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2019; 2019. https://doi.org/10.1109/ISGT.2019.8791662
74. Ma K, Zhu J, Soltani M, Hajizadeh A, Chen Z. Wind turbine down-regulation strategy for minimum wake deficit. In: 2017 Asian Control Conference,
ASCC 2017; 2018:2652-2656. https://doi.org/10.1109/ASCC.2017.8287595
75. Marden JR, Ruben SD, Pao LY. A model-free approach to wind farm control using game theoretic methods. IEEE Trans Control Syst Technol. 2013;21
(4):1207-1214. https://doi.org/10.1109/TCST.2013.2257780
76. Martinen S, Carlén I, Nilsson K, Breton S-P, Ivanell S. Analysis of the effect of curtailment on power and fatigue loads of two aligned wind turbines
using an actuator disc approach. In: Journal of Physics: Conference Series, Vol. 524; 2014:12182. https://doi.org/10.1088/1742-6596/524/1/
012182
77. Mirzaei M, Tuhfe G, Giebel G, Sorensen PE, Poulsen NK. Turbine control strategies for wind farm power optimization. In: American Control Conference; 2015. https://doi.org/10.1109/ACC.2015.7170979
78. Park J, Law KH. Cooperative wind turbine control for maximizing wind farm power using sequential convex programming. Energy Convers Manag.
2015;101:295-316. https://doi.org/10.1016/j.enconman.2015.05.031
79. Okulov VL, Mikkelsen R, Sørensen JN, Naumov IV, Tsoy MA. Power properties of two interacting wind turbine rotors. J Energy Resour Technol. 2017;
139(5):51210. https://doi.org/10.1115/1.4036250
80. Rijcke SD, Meyers J, Driesen J. Reducing power gradients in large-scale wind farms by optimal active power control. In: 2013 IEEE Grenoble Conference. IEEE; 2013:1-6. https://doi.org/10.1109/PTC.2013.6652360
81. Santhanagopalan V, Rotea MA, Iungo GV. Performance optimization of a wind turbine column for different incoming wind turbulence. Renew Energy.
2018;116:232-243. https://doi.org/10.1016/j.renene.2017.05.046
82. Serrano González J, Burgos Payán M, Riquelme Santos J, González Rodríguez AG. Maximizing the overall production of wind farms by setting the
individual operating point of wind turbines. Renew Energy. 2015;80:219-229. https://doi.org/10.1016/j.renene.2015.02.009
83. Silva JG, Doekemeijer B, Ferrari R, Wingerden J-V. Active power control of waked wind farms: Compensation of turbine saturation and thrust force
balance; 2021.
84. Su Y, Li Q, Duan B, Wu Y, Tan M, Qiao H. A coordinative optimization method of active power and fatigue distribution in onshore wind farms. Int
Trans Electr Energy Syst. 2017;27(10):1-12. https://doi.org/10.1002/etep.2392
85. Tian J, Su C, Soltani M, Chen Z. Active power dispatch method for a wind farm central controller considering wake effect. In: IECON Proceedings
(Industrial Electronics Conference); 2014:5450-5456. https://doi.org/10.1109/IECON.2014.7049333
86. Vali M, Petrovic V, Boersma S, van Wingerden JW, Kuhn M. Adjoint-based model predictive control of wind farms: beyond the quasi
steady-state power maximization. In: International Federation of Automatic Control, Vol. 1; 2017:4510-4515. https://doi.org/10.1016/j.ifacol.2017.
08.382
87. Vali M, Petrovic V, Steinfeld G, Pao LY, Kühn M. Large-eddy simulation study of wind farm active power control with a coordinated load distribution.
J Phys Conf Ser. 2018;1037(3):32018. https://doi.org/10.1088/1742-6596/1037/3/032018
88. Vali M, Petrovic V, Pao LY, Kühn M. Lifetime extension of waked wind farms using active power control. In: Journal of Physics: Conference Series,
Vol. 1256; 2019:12029. https://doi.org/10.1088/1742-6596/1256/1/012029
89. Van Binsbergen DW, Wang S, Nejad AR. Effects of induction and wake steering control on power and drivetrain responses for 10 MW floating wind
turbines in a wind farm. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022044
90. Vitulli JA, Larsen GC, Pedersen MM, Ott S, Friis-Møller M. Optimal open loop wind farm control. In: Journal of Physics: Conference Series, Vol. 1256;
2019. https://doi.org/10.1088/1742-6596/1256/1/012027
91. Wang F, Garcia-Sanz M. Wind farm cooperative control for optimal power generation. Wind Eng. 2018;42(6):547-560. https://doi.org/10.1177/
0309524X18780377
92. Yang Z, Li Y, Seem JEE. Maximizing wind farm energy capture via Nested–Loop extremum seeking control. In: Proceeding of the ASME 2013
Dynamic Systems and Control Conference; 2013. https://doi.org/10.1115/DSCC2013-3971
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
HOUCK
HOUCK
93. Zhang B, Hu W, Hou P, Soltani M, Chen Z. Wind farm active power dispatch for output power maximizing based on a wind turbine control strategy
for load minimizing. In: 2015 International Conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2015. IEEE; 2016:
1-6. https://doi.org/10.1109/SMART.2015.7399215
94. Tian J, Zhou D, Su C, Blaabjerg F, Chen Z. Maximum energy yield oriented turbine control in PMSG-based wind farm. The J Eng. 2017;2017(13):
2455-2460. https://doi.org/10.1049/joe.2017.0770
95. Göçmen T, Laan PVD, Réthoré PE, Diaz AP, Larsen GC, Ott S. Wind turbine wake models developed at the Technical University of Denmark: A
review. Renew Sustain Energy Rev. 2016;60:752-769. https://doi.org/10.1016/j.rser.2016.01.113
96. Pena A, Rathmann O. Atmospheric stability-dependent infinite wind-farm models and the wake-decay coefficient. Wind Energy. 2014;17:1269-1285.
97. Munters W, Meyers J. Effect of wind turbine response time on optimal dynamic induction control of wind farms. In: Journal of Physics: Conference
Series; 2016. https://doi.org/10.1088/1742-6596/753/5/052007
98. Munters W, Meyers J. An optimal control framework for dynamic induction control of wind farms and their interaction with the atmospheric boundary layer. Philos Trans the R Soc A. 2017;375:20160100. https://doi.org/10.1017/jfm.2015.70
99. Munters W, Meyers J. Dynamic strategies for yaw and induction control of wind farms based on large-eddy simulation and optimization. Energies.
2018;11(1):177. https://doi.org/10.3390/en11010177
100. Widnall SE. The stability of a helical vortex filament. J Fluid Mech. 1972;54:641-663.
101. Newton I. Philosophiae Naturalis Principia Mathematica. London: Global Grey; 1687.
102. Ahmad T, Basit A, Ahsan M, Coupiac O, Girard N, Kazemtabrizi B, Matthews PC. Implementation and analyses of yaw based coordinated control of
wind farms. Energies. 2019;12(7):1-15. https://doi.org/10.3390/en12071266
103. Annoni J, Bay C, Taylor T, Pao L, Fleming P, Johnson K. Efficient optimization of large wind farms for real-time control. In: 2018 Annual American
Control Conference (ACC) IEEE; 2018:6200-6205. https://doi.org/10.23919/ACC.2018.8430751
104. Archer CL, Vasel-Be-Hagh A. Wake steering via yaw control in multi-turbine wind farms: Recommendations based on large-eddy simulation. Sustain
Energy Technol Assess. 2019;33:34-43. https://doi.org/10.1016/j.seta.2019.03.002
105. Astolfi D, Castellani F, Natili F. Data-driven methods for the analysis of wind turbine yaw control optimization. J Solar Energy Eng. 2021;143(1):
14501. https://doi.org/10.1115/1.4047413
106. Bartl J, Mühle F, Sætran L. Wind tunnel study on power output and yaw moments for two yaw-controlled model wind turbines. Wind Energy Sci.
2018;3(2):489-502. https://doi.org/10.5194/wes-3-489-2018
107. Bartl J, Mühle F, Schottler J, et al. Wind tunnel experiments on wind turbine wakes in yaw: effects of inflow turbulence and shear. Wind Energy Sci.
2018;3(1):329-343. https://doi.org/10.5194/wes-3-329-2018
108. Bastankhah M, Porté-Agel F. Wind farm power optimization via yaw angle control: A wind tunnel study. J Renew Sustain Energy. 2019;11(2):23301.
https://doi.org/10.1063/1.5077038
109. Bromm M, Rott A, Beck H, Vollmer L, Steinfeld G, Kühn M. Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes. Wind Energy. 2018;21(11):1011-1028. https://doi.org/10.1002/we.2210
110. Brugger P, Debnath M, Scholbrock A, et al. Lidar measurements of yawed-wind-turbine wakes: Characterization and validation of analytical models.
Wind Energy Sci. 2020;5(4):1253-1272. https://doi.org/10.5194/wes-5-1253-2020
111. Campagnolo F, Petrovic V, Schreiber J, Nanos EM, Croce A, Bottasso CL. Wind tunnel testing of a closed-loop wake deflection controller for wind
farm power maximization. In: Journal of Physics: Conference Series, Vol. 753; 2016. https://doi.org/10.1088/1742-6596/753/3/032006
112. Campagnolo F, Weber R, Schreiber J, Bottasso CL. Wind tunnel testing of wake steering with dynamic wind direction changes. Wind Energy Sci.
2020;5(4):1273-1295. https://doi.org/10.5194/wes-5-1273-2020
113. Churchfield MJ, Fleming P, Bulder B, White SM. Wind turbine wake-redirection control at the Fishermen's Atlantic City Windfarm. In: Proceedings
of the Annual Offshore Technology Conference, Vol. 1; 2015:14-32. https://doi.org/10.4043/25644-ms
114. Ciri U, Rotea MA, Leonardi S. Effect of the turbine scale on yaw control. Wind Energy. 2018;21(12):1395-1405.
115. Cossu C. Wake redirection at higher axial induction. Wind Energy Sci. 2021;6:377-388. https://doi.org/10.5194/wes-2020-111
116. Croce A, Cacciola S, Sartori L, De Fidelibus P. Evaluation of the impact of wind farm control techniques on fatigue and ultimate loads. Wind Energy
Science Discussions [preprint], in review. 2020. https://doi.org/10.5194/wes-2019-103
117. Dahlberg J-A, Medici D. Potential improvement of wind turbine array efficiency by active wake control (AWC). In: 2003 European Wind Energy Conference and Exhibition; 2003:65-84.
118. Damiani R, Dana S, Annoni J, Fleming P, Roadman J, van Dam J, Dykes K. Assessment of wind turbine component loads under yaw-offset conditions.
Wind Energy Sci Discuss. 2017;3(1):1-25. https://doi.org/10.5194/wes-3-173-2018
119. Dhiman HS, Deb D, Muresan V, Balas VE. Wake management in wind farms: An adaptive control approach. Energies. 2019;12(7):1-18. https://doi.
org/10.3390/en12071247
120. van Dijk MT, van Wingerden JW, Ashuri T, Li Y. Wind farm multi-objective wake redirection for optimizing power production and loads. Energy.
2017;121:561-569. https://doi.org/10.1016/j.energy.2017.01.051
121. Doekemeijer B, Kern S, Maturu S, et al. Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy. Wind
Energy Sci. 2021;6:159-176. https://doi.org/10.5194/wes-2020-80
122. Doekemeijer BM, van der Hoek D, van Wingerden JW. Closed-loop model-based wind farm control using FLORIS under time-varying inflow conditions. Renew Energy. 2020;156:719-730. https://doi.org/10.1016/j.renene.2020.04.007
pez B, Díaz A, Campagnolo F, Usera G. A large eddy simulation framework to assess wind farm power maximization strate123. Draper M, Guggeri A, Lo
gies: validation of maximization by yawing. In: Journal of Physics: Conference Series, Vol. 1037; 2018. https://doi.org/10.1088/1742-6596/1037/7/
072051
124. Ennis BL, White JR, Paquette JA. Wind turbine blade load characterization under yaw offset at the SWiFT facility. In: Journal of Physics: Conference
Series, Vol. 1037; 2018. https://doi.org/10.1088/1742-6596/1037/5/052001
125. Fleming P, Gebraad P, Lee S, et al. High-fidelity simulation comparison of wake mitigation control strategies for a two-turbine case. In: ICOWES2013
Conference; 2013:194-205. https://doi.org/10.1002/we.1810
126. Fleming P, Gebraad PMO, Lee S, et al. Evaluating techniques for redirecting turbine wakes using SOWFA. Renew Energy. 2014;70:211-218. https://
doi.org/10.1016/j.renene.2014.02.015
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
218
219
127. Fleming P, Gebraad PMO, Lee S, et al. Simulation comparison of wake mitigation control for a two-turbine case. Wind Energy. 2015;18:2135-2143.
128. Fleming P, Ning A, Gebraad PMO, Dykes K. Wind plant system engineering through optimization of layout and yaw control. Wind Energy. 2015;19:
329-344.
129. Fleming P, Annoni J, Scholbrock A, et al. Full-scale field test of wake steering. In: Journal of Physics: Conference Series, Vol. 854; 2017. https://doi.
org/10.1088/1742-6596/854/1/012013
130. Fleming P, Annoni J, Shah JJ, et al. Field test of wake steering at an offshore wind farm. Wind Energy Sci. 2017;2(1):229-239. https://doi.org/10.
5194/wes-2-229-2017
131. Fleming P, Annoni J, Churchfield M, Martinez-Tossas LA, Gruchalla K, Lawson M, Moriarty P. A simulation study demonstrating the importance of
large-scale trailing vortices in wake steering. Wind Energy Sci. 2018;3(1):243-255. https://doi.org/10.5194/wes-3-243-2018
132. Fleming P, King J, Dykes K, et al. Initial results from a field campaign of wake steering applied at a commercial wind farm: part 1. Wind Energy Sci.
2019;4:1-22. https://doi.org/10.5194/wes-4-273-2019
133. Fleming P, King J, Simley E, et al. Continued results from a field campaign of wake steering applied at a commercial wind farm—part 2. Wind Energy
Sci. 2020;5(3):945-958. https://doi.org/10.5194/wes-5-945-2020
134. Fortes-Plaza A, Campagnolo F, Wang J, Wang C, Bottasso CL. A POD reduced-order model for wake steering control. In: Journal of Physics: Conference Series, Vol. 1037; 2018. https://doi.org/10.1088/1742-6596/1037/3/032014
135. Gebraad PMO, Teeuwisse FW, Van Wingerden JW, Fleming P, Ruben SD, Marden JR, Pao LY. A data-driven model for wind plant power optimization by yaw control. In: Proceedings of the American Control Conference; 2014:3128-3134. https://doi.org/10.1109/ACC.2014.6859118
136. Gebraad PMO, Teeuwisse FW, van Wingerden JW, Fleming PA, Ruben SD, Marden JR, Pao LY. Wind plant power optimization through yaw control
using a parametric model for wake effects-a CFD simulation study. Wind Energy. 2016;19(1):95-114. https://doi.org/10.1002/we.1822
137. Gomez-Iradi S, Astrain D, Aparicio M, Fernández L, Chávez R. Numerical walidation of wind plant control strategies. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022010
138. Herges TG, Berg JC, Bryant JT, White JR, Paquette JA, Naughton BT. Detailed analysis of a waked turbine using a high-resolution scanning Lidar. In:
Journal of Physics: Conference Series, Vol. 1037; 2018. https://doi.org/10.1088/1742-6596/1037/7/072009
139. van der Hoek D, Doekemeijer B, Andersson LE, Van Wingerden JW. Predicting the benefit of wake steering on the annual energy production of a
wind farm using large eddy simulations and Gaussian process regression. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/
10.1088/1742-6596/1618/2/022024
140. Howland MF, Lele SK, Dabiri JO. Wind farm power optimization through wake steering. Proc Natl Acad Sci United States of America. 2019;116(29):
14495-14500. https://doi.org/10.1073/pnas.1903680116
141. Howland MF, Ghate AS, Lele SK, Dabiri JO. Optimal closed-loop wake steering—part 1: conventionally neutral atmospheric boundary layer conditions. Wind Energy Sci. 2020;5(4):1315-1338. https://doi.org/10.5194/wes-5-1315-2020
142. Howland MF, González CM, Pena Martínez JJ, et al. Influence of atmospheric conditions on the power production of utility-scale wind turbines in
yaw misalignment. J Renew Sustain Energy. 2020;12:63307. https://doi.org/10.1063/5.0023746
143. Hulsman P, Andersen SJ, Göçmen T. Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations.
Wind Energy Sci. 2020;5(1):309-329. https://doi.org/10.5194/wes-5-309-2020
144. Jimenez A, Crespo A, Migoya E. Application of a LES technique to characterize the wake deflection of a wind turbine in yaw. Wind Energy. 2010;13:
559-572.
145. Kanev S. Dynamic wake steering and its impact on wind farm power production and yaw actuator duty. Renew Energy. 2020;146:9-15. https://doi.
org/10.1016/j.renene.2019.06.122
146. Kanev S, Bot E, Giles J. Wind farm loads under wake redirection control. Energies. 2020;13(15):1-15. https://doi.org/10.3390/en13164088
147. Kragh KA, Hansen MH. Load alleviation of wind turbine by yaw misalignment. Wind Energy. 2014;17:971-982.
148. Kretschmer M, Raach S, Taubmann J, Ruck N, Cheng PW. Wake redirection for active power control: A realistic case study. In: Journal of Physics:
Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022059
149. Kuo J, Pan K, Li N, Shen H. Wind farm yaw optimization via random search algorithm. Energies. 2020;13(4):865. https://doi.org/10.3390/
en13040865
150. Lima LAM, Blatt AK, MacHuca MN. Wind farm energy production optimization via wake steering. In: Journal of Physics: Conference Series,
Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022016
151. Lin M, Porté-Agel F. Power maximization and fatigue-load mitigation in a wind-turbine array by active yaw control: an LES study. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/4/042036
pez B, Guggeri A, Draper M, Campagnolo F. Wake steering strategies for combined power increase and fatigue damage mitigation: an LES study.
152. Lo
In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022067
153. Macri S, Aubrun S, Leroy A, Girard N. Experimental investigation of wind turbine wake dynamics during yaw variation. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022053
154. Mckay P, Carriveau R, Ting DSKSK. Wake impacts on downstream wind turbine performance and yaw misalignment. Wind Energy. 2013;16:221-234.
155. Miao W, Li C, Pavesi G, Yang J, Xie X. Investigation of wake characteristics of a yawed HAWT and its impacts on the inline downstream wind turbine
using unsteady CFD. J Wind Eng Ind Aerodyn. 2017;168:60-71.
156. Qian GW, Ishihara T. Wind farm power maximization through wake steering with a new multiple wake model for prediction of turbulence intensity.
Energy. 2021;220:119680. https://doi.org/10.1016/j.energy.2020.119680
157. Quick J, King J, King RN, Hamlington PE, Dykes K. Wake steering optimization under uncertainty. Wind Energy Sci. 2020;5(1):413-426. https://doi.
org/10.5194/wes-5-413-2020
158. Raach S, Schlipf D, Borisade F, Cheng PW. Wake redirecting using feedback control to improve the power output of wind farms. In: Proceedings of
the American Control Conference. American Automatic Control Council (AACC); 2016:1387-1392. https://doi.org/10.1109/ACC.2016.7525111
159. Schreiber J, Salbert B, Bottasso CL. Study of wind farm control potential based on SCADA data. In: Journal of Physics: Conference Series, Vol. 1037;
2018. https://doi.org/10.1088/1742-6596/1037/3/032012
160. Shapiro CR, Gayme DF, Meneveau C. Modelling yawed wind turbine wakes: A lifting line approach. J Fluid Mech. 2018;841:R11-R112. https://doi.
org/10.1017/jfm.2018.75
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
HOUCK
HOUCK
161. Simley E, Fleming P, King J. Design and analysis of a wake steering controller with wind direction variability. Wind Energy Sci Discuss. 2019;5(2):1-26.
https://doi.org/10.5194/wes-2019-35
162. Uemura Y, Tanabe Y, Mamori H, Fukushima N, Yamamoto M. Wake deflection in long distance from a yawed wind turbine. J Energy Resour Technol,
Trans ASME. 2017;139(5):1-9. https://doi.org/10.1115/1.4036541
163. Zalkind DS, Pao LY. The fatigue loading effects of yaw control for wind plants. In: Proceedings of the American Control Conference; 2016:537-542.
https://doi.org/10.1109/ACC.2016.7524969
164. Zong H, Porté-Agel F. Experimental investigation and analytical modelling of active yaw control for wind farm power optimization. Renew Energy.
2021;170:1228-1244. https://doi.org/10.1016/j.renene.2021.02.059
165. Gebraad PMOMO, van Wingerden JW. Maximum power-point tracking control for wind farms. Wind Energy. 2015;18:429-447.
166. Bossanyi E. Combining induction control and wake steering for wind farm energy and fatigue loads optimisation. In: Journal of Physics: Conference
Series, Vol. 1037; 2018. https://doi.org/10.1088/1742-6596/1037/3/032011
167. Castillo R, Bayne S, Pol S, Westergaard C. Wind turbine wake position detection and rotor speed-based wake steering validation in a wind tunnel
wake simulator. Wind Eng. 2020;44(5):483-493. https://doi.org/10.1177/0309524X19852350
168. Park J, Kwon S, Law KH. Wind farm power maximization based on a cooperative static game approach. In: SPIE Smart Structures/NDE Conference,
Vol. 8688; 2013:1-15. https://doi.org/10.1117/12.2009618
169. Park J, Kwon SD, Law K. A data-driven, cooperative approach for wind farm control: A wind tunnel experimentation. Energies. 2017;10(7):1-17.
https://doi.org/10.3390/en10070852
170. Churchfield MJ, Lee S. SOWFA; 2012.
171. Brown K, Houck D, Maniaci D, Westergaard C, Laboratories SN. Rapidly recovering wind turbine wakes with dynamic pitch and rotor speed control.
In: AIAA SciTech Forum; 2021:1-16. https://doi.org/10.2514/6.2021-1182
172. Goit JP, Munters W, Meyers J. Optimal coordinated control of power extraction in LES of a wind farm with entrance effects. Energies. 2016;9:29.
https://doi.org/10.3390/en9010029
173. Ylmaz AE, Meyers J. Optimal dynamic induction control of a pair of inline wind turbines. Phys Fluids. 2018;30(8):85106. https://doi.org/10.1063/1.
5038600
174. Cacciola S, Bertozzi A, Sartori L, Croce A. On the dynamic response of a pitch/torque controlled wind turbine in a pulsating dynamic wake. J Phys
Conf Ser. 2020;1618(062033). https://doi.org/10.1088/1742-6596/1618/6/062033
175. Frederik JA, Doekemeijer BM, Mulders SP, van Wingerden JW. The helix approach: Using dynamic individual pitch control to enhance wake mixing
in wind farms. Wind Energy. 2020;23:1739-1751. https://doi.org/10.1002/we.2513
176. Kimura K, Iida M, Tanabe Y, Matsuo Y. Forced wake meandering for rapid recovery of velocity deficits in a wind turbine wake. In: In AIAA Scitech
2019 Forum; 2019:2083. https://doi.org/10.2514/6.2019-2083
177. Marten D, Paschereit CO, Huang XX, Meinke M, Schröder W, Müller JS, Oberleithner K. Predicting wind turbine wake breakdown using a free vortex
wake code. In: AIAA Scitech 2019 Forum; 2019:4672-4685. https://doi.org/10.2514/6.2019-2080
178. Munters W, Meyers J. Optimal dynamic induction and yaw control of wind farms: Effects of turbine spacing and layout. In: Journal of Physics: Conference Series, Vol. 1037; 2018:32015.
179. Munters W, Meyers J. Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and
sinusoidal induction control of first-row turbines. Wind Energy Science. 2018;3:409-425.
180. Wang C, Campagnolo F, Sharma A, Bottasso CL. Effects of dynamic induction control on power and loads, by LES-ALM simulations and wind tunnel
experiments. In: Journal of Physics: Conference Series, Vol. 1618; 2020. https://doi.org/10.1088/1742-6596/1618/2/022036
181. Ashton R, Viola F, Camarri S, Gallaire F, Iungo GV. Hub vortex instability within wind turbine wakes: Effects of wind turbulence, loading conditions,
and blade aerodynamics. Phys Rev Fluids. 2016;1:73603. https://doi.org/10.1103/PhysRevFluids.1.073603
182. Odemark Y, Fransson JHM. The stability and development of tip and root vortices behind a model wind turbine. Exp Fluids. 2013;54(9):1-16. https://
doi.org/10.1007/s00348-013-1591-6
183. Okulov VL, Sørensen JN. Stability of helical tip vortices in a rotor far wake. J Fluid Mech. 2007;576:1. https://doi.org/10.1017/
S0022112006004228
184. Quaranta HU, Bolnot H, Leweke T. Long-wave instability of a helical vortex. J Fluid Mech. 2015;780:687-716. https://doi.org/10.1017/jfm.2015.479
185. Sarmast S, Dadfar R, Mikkelsen RF, Schlatter P, Ivanell S, Sørensen JN, Henningson DS. Mutual inductance instability of the tip vortices behind a
wind turbine. J Fluid Mech. 2014;755:705-731. https://doi.org/10.1017/jfm.2014.326
186. Sherry M, Nemes A, Lo Jacono D, Blackburn HM, Sheridan J. The interaction of helical tip and root vortices in a wind turbine wake. Phys Fluids.
2013;25(11):117102. https://doi.org/10.1063/1.4824734
187. Viola F, Iungo GV, Camarri S, Porté-Agel F, Gallaire F. Instability of wind turbine wakes immersed in the atmospheric boundary layer. In: Journal of
Physics: Conference Series, Vol. 625; 2015:12034. https://doi.org/10.1088/1742-6596/625/1/012034
188. Bottasso CL, Croce A, Savini B, Sirchi W, Trainelli L. Aero-servo-elastic modeling and control of wind turbines using finite-element multibody procedures. Multibody Syst Dyn. 2006;16(3):291-308. https://doi.org/10.1007/s11044-006-9027-1
189. Guntur S, Jonkman J, Sievers R, Sprague MA, Schreck S, Wang Q. A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades. Wind Energy Sci. 2017;2(2):443-468. https://doi.org/10.5194/wes-2-443-2017
How to cite this article: Houck DR. Review of wake management techniques for wind turbines. Wind Energy. 2022;25(2):195-220. doi:
10.1002/we.2668
10991824, 2022, 2, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/we.2668 by Izmir Yuksek Teknoloji Enstit, Wiley Online Library on [16/12/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
220