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Development of MATLAB/SIMULINK Models for PV and Wind Systems and
Review on Control Strategies for Hybrid Energy Systems
Article in International Review on Modelling and Simulations · August 2012
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International Review on Modelling and Simulations (I.RE.MO.S.), Vol. 5, N. 4
ISSN 1974-9821
August 2012
Development of MATLAB/SIMULINK Models for PV
and Wind Systems and Review on Control Strategies
for Hybrid Energy Systems
T. Bogaraj, J. Kanakaraj
_____________________________________________________________________________
Abstract – Depletion of fossil fuel resources and environmental pollution caused by them
necessitates alternate energy sources for electricity production. In the recent past renewable
energy sources are considered as alternative for the fossil fuel energy sources. Photovoltaic and
Wind are the more promising renewable energy sources and are widely used in many countries for
standalone applications or connected to Utility Grid. The unpredictable pattern of natural
resources requires combined utilization of these sources for providing uninterrupted and reliable
power supply to the consumers. A MATLAB/SIMULINK model for 10 kW Solar PV system has
been developed and its characteristics are presented. The characteristics of Wind turbine is also
simulated and results are presented. Further this paper presents the review of current state of
control strategies for hybrid energy system in standalone mode and grid connected mode. Also the
future developments of such system and their recognition by the industrial and home users are
discussed. Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved.
Keywords: Energy Management, Hybrid Power Systems, MPPT, Power Conversion, Solar
Energy, Wind Energy
______________________________________________________________________________
Nomenclature
a
IPV, cell
I0,cell
Isc
Voc
q
k
T
Tn
Ipv
I0
Nss
Npp
Rs
Rp
Pmax,m,
Pmax,e
Vt
Vmp
Imp
Ipv,n
Isc,n
Voc,n
T
Ideality or completion factor
Current generated by the incident light [A]
Reverse saturation/leakage current of the diode
[A]
Short circuit current of PV array[A]
Open circuit voltage of PV array [V]
Electron charge (1.60217646 × 1019 C)
Boltzmann constant (1.3806503 × 1023 J/K)
Temperature of the p–n junction [K]
Nominal temperatures [K]
Photovoltaic (PV) [A]
Saturation current [A]
Number of cells connected in series
Number of cells connected in parallel
Series resistance of PV cell [ ]
Parallel resistance of PV cell [ ]
Maximum power calculated by the I–V model
of PV array [W]
Maximum experimental power o f PV array
from the datasheet [W]
Thermal voltage of the array [V]
Voltage at maximum power point [V]
Current at maximum power point [A]
Light generated current at the nominal
condition (usually 250C and 1000 W/m2)
Short circuit current of PV array at the
nominal condition [A]
Open circuit voltage of PV array at the
nominal condition [V]
Manuscript received and revised July 2012, accepted August 2012
G
Gn
R
Vw
ref
T – Tn
Irradiation on the device surface [W/m2]
Nominal irradiation [W/m2]
Air density ( =1.225 kg/m3)
Blade length [m]
Wind speed [m/s]
Power coefficient
Maximum value of power coefficient
Pitch angle [Mechanical Degrees]
Tip speed ratio
Optimal value of the tip speed ratio
Rotational speed of the wind turbine [rad/s]
Reference speed of the wind turbine [rad/s]
I.
Introduction
A Hybrid power system is one in which the electrical
load is served by two or more power sources. Because
renewable energy sources, such as Solar and Wind, are
inherently intermittent, frequently they are combined
with dispatchable power sources such as utility grid and
diesel generators, to ensure the continuous supply of
power. Energy storage is often included in hybrid
systems that can deliver certain amount of energy on
power demand until its stored energy is exhausted, but it
must be recharged at that point.
Around the world, thousands of communities and
industrial sites are not powered by utility grid. The
traditional means of electrification in such locations is
Diesel generation, because the initial cost is low and
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
1701
T. Bogaraj, J. Kanakaraj
there are many people trained in operation and
maintenance. As fuel cost rise and environmental
concern mounts, there is increasing interest in the design
and usage of hybrid power systems during last two
decades
such
as
PV/Diesel,
Wind/Diesel,
PV/Wind/Diesel, PV/Fuel cell, Wind/Fuel cell,
PV/Wind/Fuel cell etc.
Recent research and developments in Solar PV and
Wind energy Technologies [1] and increase in the cost of
conventional energy sources make the renewable hybrid
energy systems cost effective in the near future. Since
they are clean and green, the application of such systems
will increase and accepted worldwide. Hybrid energy
systems can significantly reduce the total life cycle cost
of stand-alone power supplies in many situations, while
at the same time providing a more reliable supply of
electricity through the combination of energy sources [2].
Hybrid Systems Performance Assessment is one of the
key issues for the development of Hybrid systems [3].
Lack of confidence of reliability is a major barrier for
market development.
Hybrid power systems [4] are being operated by
multi-sources and decentralized distribution of
subsystems means to achieve better energy management
and control is one of hot topics of research. In Literature
many different control strategies have been considered.
Hybrid power systems could be considered for
distributed generation using renewable energy sources.
Such systems address the following issues:
Reduce the peak demand
Greatly reduce the transmission losses and improve
the reliability of the grid network.
To Remote and inaccessible areas of the country
extension of the grid may not be economically
feasible. In such cases distributed generation can play
a major role.
The modular nature of distributed generation system
coupled with low gestation period enables the easy
capacity additions when required.
Disruptions such as grid failure, etc., can be
prevented as electricity is produced close to the
consumer.
The quality of power (voltage and frequency) can
also be maintained easily.
Distributed generation systems offer the possibility of
combining energy storage and management systems.
Inadequacies in distribution network have been one of
the major reasons for poor supply of power.
Distributed generation facilitates an optimal use of
the grid that improves the reliability of the grid
network and reduces the congestion.
The configuration of the Hybrid power system
considered in this paper is shown in the Fig.1. For the
stand alone mode the grid connection is avoided in the
figure. In its structure, it consists of energy conversion
devices, MPPT controllers, power electronic converters,
and Energy management controller.
The advances in power electronic devices and
systems, communication technologies, software and
computer technologies led to the development of
research in the following areas of Hybrid Power system:
Improvement of efficiency of photovoltaic cells
Development of models for PV, Wind and/or Hybrid
Power Systems
Design of MPPT control strategies for Hybrid Power
Systems
Development of Energy management control
strategies to utilize the available power, control the
power flow between sources, reliable supply of power
and power quality
Finding the size (capacity) of sources for particular
application to reduce the initial cost and running cost
Integration with fuel cell and ultra capacitor to
improve the system reliability and power quality [30]
WG
AC
DC BUS
DC
LOAD
MPPT
PV
DC
AC
GRID
DC
DC
MPPT
BATTERY
ENERGY
MANAGEMENT
CONTROLLER
Fig. 1. Configuration of PV/Wind Hybrid power system
This paper aims to present the current state of
technologies for Hybrid Power System in the context of
modeling, energy management, and Maximum Power
Point Tracking.
II.
Modeling of System Components
II.1.
PV Module
A simulation must be performed for system analysis
and parameter settings during design process of PV
powered systems. Therefore an efficient user friendly
simulation model of the PV Arrays is always needed.
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
Fig. 2. Generalized model of a PV cell
International Review on Modelling and Simulations, Vol. 5, N. 4
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T. Bogaraj, J. Kanakaraj
The model is developed by using equivalent circuit of
PV cell. From the theory of Photovoltaic system the
basic equation that mathematically describes I-V
characteristic of the ideal photovoltaic cell shown in
Fig. 2 [5]-[7] is:
Imp) point of the I -V curve, i.e. the maximum power
calculated by the I -V model of (2), Pmax,m, is equal to the
maximum experimental power from the datasheet, Pmax,e,
at maximum power point (MPP). The Rs and Rp may be
found by making Pmax,m= Pmax,e to get the I-V and P-V
curve model fits with the experimental data:
(1)
where Ipv,cell is the current generated by the incident light,
Id is the Shockley diode equation, I0,cell is the reverse
saturation or leakage current of the diode, q is the
electron charge (1.60217646 × 10-19 C), k is the
Boltzmann constant (1.3806503 × 10-23 J/K), T (in
Kelvin) is the temperature of the p–n junction, and a is
the diode ideality constant.
Practical arrays are composed of several photovoltaic
cells that are connected in series and parallel. Cells
connected in parallel increase the output current and cells
connected in series increase output voltage.
The
observation of the characteristics at the terminals of the
photovoltaic array requires [5] additional parameters to
the basic equation (1). So it can be expressed as [8]:
(2)
where Ipv and I0 are the photovoltaic (PV) and saturation
currents, respectively, of the array and Vt= NsskT/q is the
thermal voltage of the array with Nss cells connected in
series and Npp is number of cells connected in parallel.
The I-V characteristic of the photovoltaic device
depends on the internal characteristics of the device (Rs,
Rp) and on external influences such as irradiation level
and temperature:
(5)
(6)
It shows that for any value of Rs there will be a value
of Rp that makes the mathematical I-V curve across the
experimental (Vmp, Imp) point. This equation must be
solved by a numerical method. In this paper NewtonRaphson method is used. In the iterative process Rs
must be incremented from Rs = 0.
Adjusting the P-V curve to match the experimental
data in the data sheet requires finding the curve for
several values of R s and R p . This iterations are done
until P ma x,m = P ma x,e . By increasing Rs the peak power
(Pmax, m) moves towards the Maximum Power Point of the
P-V curve. For every P-V curve, there is a corresponding
I-V curve that crosses the experimental MPP at (Vmp,
Imp). Further improvement of a model can be done by
introducing the equation:
(3)
It is difficult to determine the light-generated current
(Ipv) of the elementary cells, without the influence of the
series and parallel resistances. The diode saturation
current I0 to improve photovoltaic model is described by:
(7)
Every iteration updates the Rs and Rp towards the best
solution. In order to get the best solutions Rs and Rp
values are initialized. Initial value of Rs may be zero:
(8)
(4)
Similarly the initial value of Rp may be given by:
This equation matches the open-circuit voltages of the
model with the experimental data for a very large range
of temperatures.
II.2.
Adjusting the Model Parameters (Rs and Rp)
This paper proposes a method for adjusting Rs and
Rp based on the fact that there is an only pair {Rs, Rp}
that ensures [5] that Pmax,m=Pmax,e=Vmp. Imp at the (Vmp.
(9)
It determines the minimum value of Rp, which is the
slope of the line segment between points of short-circuit
and the maximum-power in the I-V curve of the solar
panel.
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
1703
T. Bogaraj, J. Kanakaraj
The TITANS6_60 specifications are tabulated in
Table I. The values of Rs and Rp found from the iterated
method are given in Table II.
The photovoltaic array has been simulated with an
equivalent circuit model of a photovoltaic system using
MATLAB/SIMULINK environment which is shown in
Fig. 3.
Fig. 5. P-V curve for TITANS6_60
Fig. 3. Generalized Simulation model of Photovoltaic system
TABLE I
PARAMETERS OF THE TITANS6_60 SOLAR ARRAY AT 250C, 1000 W/M2
REFERRED FROM DATA SHEET
Iscn
8.21A
Vocn
37.0V
Imp
7.44A
Vmp
28.9V
Pmax,e
215.016W
Kv
-0.123V/K
Ki
3.18x10-3 A/K
Ns
60
TABLE II
PARAMETERS OF T HE MODEL OF THE TITANS6_60 SOLAR ARRAY AT
NOMINAL OPERATING CONDITION OBTAINED FROM NUMERICAL
METHOD
Iscn
8.21A
V ocn
37.0V
Imp
7.44A
Vmp
28.9V
Pmax,e
215.016W
Ion
2.90204x10-10A
Ipv
8.264950A
a
1
Rs
0.438800
Rp
65.570456
Fig. 6. I-V Curve for different Irradiation Levels at 25oC
Fig. 7. P-V Curve for different Irradiation Levels at 25oC
Fig. 4. I-V curve for TITANS6_60
Fig. 4 and Fig. 5 shows the P-V and I-V curve of one
module of TITANS6_60 solar array for the Rs and Rp
values found.
This model is used to study the model for a 10 kW
solar PV system installed at PSG College of Technology,
Coimbatore, India which consists of two parallel
combinations of 6 series and 4 parallel arrays.
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
1704
T. Bogaraj, J. Kanakaraj
II.3.
Wind Energy System
The aerodynamic power converted by the wind turbine
is depending on the power coefficient such as [9],[10]:
(10)
where is the air density ( =1.225 kg/m3), R is the blade
length and Vw is the wind speed.The power coefficient is
depending on the pitch angle and the tip speed ratio
given by:
(11)
Fig. 8. I-V curve for Different Temperature Levels at 1000W/m2
where is the rotational speed of the wind turbine and
generator. The power coefficient is given by the
expression:
(12)
with:
The
characteristics of the wind turbine for
different pitch angles were obtained from the simulation
in Matlab and is shown in Fig. 10.
Fig. 9. P-V curve for Different Temperature Levels at 1000W/m2
Simulated results for 10 kW PV system is shown in
Fig. 6 to Fig. 9. I-V and P-V curve are shown in Fig. 6
and Fig. 7 for the various irradiation conditions at 25 0C.
I-V and P-V curves are shown in Fig. 8 and Fig. 9
obtained for various temperature conditions at irradiation
level of 1000W/m2. These results fit with the
experimental data shown in Table III obtained from the
installed 10 kW PV system. This model is fairly accurate
and is used for design Hybrid system in the further
research work.
TABLE III
EXPERIMENTAL RESULTS OF 10 KW PV SYSTEM USING
TITANS6_60
Temp
(0C)
Irradiation
(W/m2)
26.3
27.2
28.6
28.9
29.0
29.8
30.2
31.6
250
520
630
640
650
570
480
310
Solar
Power
(kWh )
3.3
5.2
6.2
6.4
6.0
5.6
4.5
2.9
Voltage
(V)
177
152
140
130
133
136
137
136
Current
(A)
18.6
34.4
45.6
49.0
45.0
41.2
32.8
21.3
Fig. 10. Tip speed ratio Vs Power coefficient at different pitch angles
From the characteristics it is perceived that there exist
the maximal value of power coefficient
corresponding to the optimal value of the tip speed ratio
for each value of pitch angle . The maximal power
can be expressed as:
(13)
The speed reference for maximizing the power
captured by the wind (MPPT strategy) is given by:
(14)
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
1705
T. Bogaraj, J. Kanakaraj
From the Fig.11 it is clear that the maximum power
can be achieved at different rotor speeds for different
power curves. The rotor speed is adjusted to achieve the
Maximum Power Point operation of the Wind Turbine.
Fig. 11. Turbine speed Vs Power at different wind velocities
III. Control Strategies
Hybrid Energy Systems has the problem of controlling
energy flow between sources and loads, in spite of nonlinear variation of natural resources and continuously
changing loads [11].
The Control system should meet the load demand as
well as maintain power quality and stability. The main
requirements of power management strategy [2] for
hybrid energy system are to satisfy the load demand
under variable weather conditions and to manage the
power flow while ensuring efficient operation of the
different energy systems. In Literature there are several
control strategies for MPPT and Energy management
between sources and load. Some of the methods are
discussed in this section.
III.1. Energy Management Control Strategies
P. J. R. Pinto and C. M. Rangel have developed a
power management strategy [2] for a PV/FC hybrid
energy system that optimizes the overall system
performance. It was stated that simulation results shows
that the control strategy is effective in protecting the
battery bank from overutilization and properly regulating
the operating pattern of the Fuel cell and electrolyzer.
Further simulation studies need to be performed in order
to optimize the dc bus voltage thresholds of the
hysteresis band on this system [30].
Guangming LI et al. have designed a hardware
Controller [4] which is composed of PLC, HMI, gridconnected control module, ac multi-function electric
power meters, dc electric power meters, RS485/TCP
converter etc. This is applicable for small and Medium
HPS and can run under grid connection or stand alone
mode.
M. Uzunoglu, O.C. Onar, M.S. Alam [11] have
developed model of a stand-alone PV/FC/UC hybrid
power system for residential micro-grid users with
appropriate power flow controllers in MATLAB and
Simpowersystems environment. The dynamic behavior
of the hybrid system is tested under varying solar
radiation and load demand conditions where the solar
radiation and power demand data are based on real-world
records. The developed system and its control strategy
exhibit excellent performance for the simulation of a
complete day or longer periods of time.
Shuyun Jia, Jiang Chang [12] established a Multiagent model (MACMWSHPS) in Wind-solar Energy
Hybrid Power System, which includes data collecting
and processing module, control module, infomanagement module, resource-management module,
wind and solar energy power generation agent modules,
inverter agent etc. This technology is the application of
Artificial Intelligence combined with network
technology. The multi-agent technology can solve some
control difficulties such as low reliability, weak
maintainability resulting from the system’s nonlinear,
distributing and random features. The authors reported
that it can optimize the system and make the system’s
output more stable, performance better, antijamming
ability stronger.
Yi-Feng Wang, et al. [13] defines a Hierarchical
Fuzzy logic control for energy management is less
complex and fast. The three state battery charging by
using Fuzzy-PID control [14] was discussed, in which
Grouping of batteries into two, made the possibility of
charging and discharging simultaneously ie, one group
supplying the load and the other is charging when the
SOC is minimum.
Caisheng Wang and M. Hashem Nehrir [15] have
developed the simulation model of ac-linked
PV/Wind/Fuelcell hybrid system for stand-alone
applications using MATLAB/Simulink. An overall
power management strategy is designed for the proposed
system to manage power flows among the different
energy sources and the storage unit in the system.
Simulation studies have been carried out to verify the
system performance under different scenarios using the
practical load profile in the Pacific Northwest regions
and the real weather data collected at Deer Lodge, MT.
A. M. Sharaf et al. [16] proposed a novel control
system for control of hybrid wind/photovoltaic (PV) farm
utilization with alternative power source for DC type
loads
which
have
been
simulated
using
MATLAB/Simulink/Simpower systems. The controller
consisting of six different loop controllers is mainly used
to regulate the DC-DC converter to reduce a weighted
total sum of all loop errors, to mainly track a given speed
reference trajectory depicting the demand for discharge
or flow and also to ensure power quality, reliability and
stability. The control strategy is based on source-load
matching that is fully suitable for hybrid
wind/photovoltaic farm with alternative interface
connection to the local electric grid.
The BP algorithm based ANN model [17] has been
proposed for the energy management in PV/Wind Hybrid
system. Training patterns are presented repeatedly to the
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
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T. Bogaraj, J. Kanakaraj
ANN model and the adjustment is performed after each
iteration whenever the network’s computed output is
different from the desired output.
This process continues until weights converge to the
desired error level or the output reaches an acceptable
level. Tamer T.N. et al. [18] proposed the control
strategy for Renewable Energy Hybrid Source (HRES),
which is decomposed into two parts, a local control
depending on the power structure of each source and a
global control deduced from global considerations and
power objectives. The complete control device, playing
the role of supervisor developed in this work, was
qualified to control and coordinate the operation of the
different subsets. The simulation results showed the
effectiveness of the adopted control strategy.
A grid connected PV/Wind operated Induction
Generator system [19] has been simulated in Simpower
of MATLAB. The proposed Average Current Mode
Controller (ACMC) for the inverter connecting PV to the
grid makes it possible for supplying reactive power to the
wind generator and to act as real power source. The
model for small stand-alone AC system with fuel cell and
solar panel [20] was implemented and simulated with
MATLAB/Simulink. The results show the feasibility of
including dynamic models of the renewable energy
resources in the analysis of the system performance.
In order to increase the reliability of Hybrid power
system, Majid Nayeripour and Mohammad Hoseintabar
[21] included Fuel Cell and Super capacitor for energy
storage. Proposed system was simulated for a day and
real weather data. The slow dynamics of Fuel Cell
system actuator makes the Fuel Cell sluggish and it
cannot change its power to desired value as fast as the
load variation [29], [30].
The dynamic response of fuel valve was simulated as
first lead-lag transfer function and this delay time was
applied to the power reference to keep the utilization
factor of Fuel Cell system at its optimal value to improve
Fuel Cell system lifetime. A new control strategy based
on optimal usage of Fuel Cell system was proposed and
it can prolong FC system lifetime and can improve its
performance.
III.2. MPPT Control Methods for Solar PV
The solar cell V-I characteristics is nonlinear and
varies with irradiation and temperature. But there is a
unique point on the V-I and P-V curves, called as the
maximum power point (MPP), at which the PV system is
said to operate with maximum efficiency and produces
its maximum power output. The location of the MPP is
not known but can be traced by either through calculation
models or search algorithms. Thus, maximum power
point tracking (MPPT) techniques are needed to maintain
the PV array’s operating point at its MPP. Many MPPT
techniques have been proposed in the literature in which
the techniques vary in many aspects, including
simplicity, convergence speed, hardware implementation
and range of effectiveness.
However, the most widely used MPPT technique is
the perturbation and observation (P&O) method [22],
[31]. The proposed new controller scheme [22] for the
standalone PV system controls both the boost converter
and the charge controller. The simple MPPT algorithm is
based on the measurement of load voltage and the PV
voltage. Depending on these two observations, the
controller will generate a suitable duty cycle for the
Converter which is given by:
(15)
Also a PWM based charge controller which controls
the charging current is used to protect the batteries.
Fuzzy logic control [23] is employed to achieve
maximum power tracking for both PV and wind energies
and to deliver this maximum power to a fixed dc voltage
bus from the considered site data at Al-Hammam. YuenHaw Chang and Chia-Yu Chang [24] described a Scaling
Fuzzy Logic Control for MPPT, which is modified fuzzy
logic control. The control algorithm was implemented in
OrCAD Pspice for the cases of steady-state response,
dynamic response to the variation of solar-cell current or
load (RL). The simulation results were illustrated to
show the efficacy of the proposed scheme. The authors
have designed hardware for Scaling Fuzzy Logic Control
and the results have not been verified at that time.
III.3. MPPT Control Methods for Wind Turbine
The maximum power output [25] can be manipulated
upon the control of the rotor speed of wind power
generator. As wind power is varying with wind velocity,
the power acquired by the wind generator is maximized
by either using Blade Pitch Control or Power Converters
on generator side.
The power converters change the duty cycle to vary
the output power of the generator according to the wind
speed. Variation of duty cycle is achieved by
conventional methods such as Tip Speed Ratio Control,
Hill climb Search method or Power Signal Feedback
control. Nowadays more researchers are employing the
techniques such as Fuzzy, Neural Network Genetic
Algorithm and Particle Swarm Optimization.
Chun-Yao Lee et al. proposed a method [25] based on
Artificial Neural Network (ANN) and particle swarm
optimization (PSO) for tracking the maximum power
point of wind power generator. The estimated wind speed
not only replaces the measurement of anemometer but
also solves the problems such as aging anemometer and
moved position. ANN and PSO were applied to estimate
and control the optimal rotor speed so as to obtain the
maximum power output of wind power generator by
considering simultaneous variation of load and wind
speed. The problem of maximizing [26] the WG output
power using the converter duty cycle as a control
variable was effectively solved using the steepest ascent
method according to the following control law:
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
1707
T. Bogaraj, J. Kanakaraj
(16)
where Dk and Dk-1 are the duty-cycle values at iterations k
and k - 1, respectively (0 < Dk < 1); Pk-1/ D k-1 is the
WG power gradient at step k - 1; and C1 is the step
change. Maximum-power-point tracking (MPPT) system
presented, consisting of a high efficiency buck-type dc/dc
converter and a microcontroller-based control unit
running the MPPT functions.
MPPT control algorithms for optimal wind energy
capture using radial basis function network (RBFN) and
torque observer MPPT algorithm [27] have been
proposed by the authors for WECS which uses PMSG as
prime mover. It was found that the PI method with torque
observer can almost operate at the optimal C p. Torque
observer is very efficient, especially for an optimization
problem that the objective function cannot be explicitly
expressed. This technique can maintain the system
stability and reach the desired performance even with
parameter uncertainties.
The hybrid algorithm [28] developed, taking the
generator output voltage value as the control measure and
calculating power variations, which is given to the duty
cycle of the converter. The duty cycle of the buck
converter is varied according to the wind speed which
varies the generator output power. This method is always
operating at MPOP (Maximum Power Operating Point)
under varying load and environmental conditions and is
easy to implement. This algorithm overcomes the
disadvantages of Perturbation & Observation method and
Incremental conductance method. The average output
power is increased when compared to the above said
methods.
IV.
Conclusion
improvements in renewable energy technologies provide
more potential for wider applications of Hybrid Energy
Systems.
To reduce the generation cost of electricity, certain
R&D improvements in solar PV and wind generator
technologies are required. Many Researchers have
simulated the control strategies using Artificial
Intelligence Methods. Such methods have to be
implemented in real time applications of PV and Wind
energy systems. This may improve the ease of
implementation & integration with the system and
reduction in cost of the control system.
References
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
Very Recent Literatures were considered for the
survey of MPPT methods for Solar PV and Wind Energy
systems separately and as a Hybrid Energy System the
Energy management control methods. A 10 kW Solar PV
system and Wind generator systems have been simulated
in MATLAB/Simulink and the simulated results are
presented. These systems will be integrated to form a
PV/wind Hybrid Energy System for further research.
Hybrid Energy systems consisting of PV and Variable
speed Wind generator are very much useful for the
remote communities, telecommunication stations, water
pumping system and industries which are far away from
the Grid, even though it is not cost effective when
compare to fossil fuels at present. Energy management
controller and MPPT control system for energy sources
may be designed and integrated to achieve efficient
energy management and Maximum power point
operation respectively. This also ensures continuous and
reliable power to the consumers and more available
energy extraction in natural resources.
Need for pollution free energy sources, ever
increasing cost of fossil fuels and comparable
[9]
[10]
[11]
[12]
[13]
[14]
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
Pragya Nema, R.K. Nema, Saroj Rangnekar, A current and future
state of art development of hybrid energy system using wind and
PV-solar: A review, Renewable and Sustainable Energy Reviews,
vol. 13, pp. 2096-2103, 2009.
P. J. R. Pinto, C. M. Rangel, A Power Management Strategy for a
Stand-Alone Photovoltaic/Fuel Cell Energy System for a 1kW
Application, Hydrogen Energy and Sustainability- Advances in
Fuel Cells and Hydrogen Workshop, Apr.2010.
Luis Arribas, Luis Cano, Ignacio Cruz, Montserrat Mata, Ermen
Llobet, PV–wind hybrid system performance: A new approach
and a case study, Renewable Energy, Vol. 35, pp. 128–137, 2010.
Guangming LI, Yuanrui CHEN, Tao LI, The Realization of
Control Subsystem in the Energy Management of Wind/Solar
Hybrid Power System, 3rd International Conference on Power
Electronics Systems and Applications, 2009.
Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto
Ruppert Filho, Comprehensive Approach to Modeling and
Simulation of Photovoltaic Arrays, IEEE Transactions on Power
Electronics, Vol. 24, No. 5, pp. 1198-1208, May 2009.
Yuncong Jiang, Jaber A. Abu Qahouq and I. Batarseh, Improved
Solar PV Cell Matlab Simulation Model and Comparison, IEEE
International Symposium on Circuits and Systems (ISCAS), pp.
2770 – 2773, May 30-June 2010, Paris.
Adel El Shahat, “PV Cell Module Modeling & ANN Simulation
for Smart Grid Applications” Journal of Theoretical and Applied
Information Technology, Vol. 16. No. 1, pp.1-15, 2010.
Huan-Liang Tsai, Ci-Siang Tu, and Yi-Jie Su, Development of
Generalized Photovoltaic Model Using Matlab/Simulink, World
Congress on Engineering and Computer Science (WCECS),
October 22 - 24, 2008, San Francisco, USA.
Aziz Remli, Djamal Aouzellag, and Kaci Ghedamsi, Study and
Control of Wind Energy Conversion System based Permanent
Magnet Synchronous Generator connected to the Grid,
International Journal of Research and Reviews in Computing
Engineering, Vol. 1, No. 1, pp.29-34, March 2011.
Aryuanto Soetedjo, Abraham Lomi, Widodo Puji Mulayanto,
Modeling of Wind Energy System with MPPT Control,
International Conference on Electrical Engineering and
Informatics, 17-19 July 2011, Bandung, Indonesia.
M. Uzunoglu, O.C. Onar, M.S. Alam, Modeling, control and
simulation of a PV/FC/UC based hybrid power generation system
for stand-alone applications, Renewable Energy, Vol. 34, pp. 509–
520, 2009.
Shuyun Jia, Jiang Chang, A multi-agent control method and
realization in wind-solar hybrid power generation system,
International Conference on Energy and Environment
Technology, 2009, pp. 507-510.
Yi-feng Wang, Jian Wu, Jun-min Xu,Yun Zhang and Ning Chen,
Wind/Photovoltaic Hybrid Power Generator Based on Fuzzy
Control, Power Electronics and Motion Control Conference
(IPEMC2009), May 2009, pp. 2284 – 2287.
Changan Ji, Xiubin Zhang, Guohui Zeng, Bin He and Xuelian
Zhou, The Application of Fuzzy Technology to Hybrid
Photovoltaic-wind
Renewable
Energy
Sources,
5th
WSEAS/IASME Int. Conf. on Electric Power Systems, High
International Review on Modelling and Simulations, Vol. 5, N. 4
1708
T. Bogaraj, J. Kanakaraj
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
Voltages, Electric Machines, Tenerife, Spain, December 16-18,
2005, pp 474-479.
Caisheng Wang, and M. Hashem Nehrir, Power Management of a
Stand-Alone Wind/Photovoltaic/Fuel Cell Energy System, IEEE
Transactions on Energy Conversion, Vol.23, No.3, pp. 957-967,
Sep. 2008.
A. M. Sharaf, I. H. Altas and E. Ozkop, A Novel Multi-loop
Fuzzy Logic Dynamic Controller for Wind/Photovoltaic-Grid
Interface DC Energy Utilization Farm, International Conference
on Renewable Energies and Power Quality (ICREPQ’09),
Valencia (Spain), 2009.
Hanaa M. Farghally, Faten H. Fahmy1, and Mohamed A. H. ElSayed, Control and Optimal Sizing of PV-Wind Powered Rural
Zone in Egypt, The Online Journal on Power and Energy
Engineering (OJPEE), Vol.2, No.2, pp. 188-195, Apr.2011.
Lotfi KRICHEN, Modeling and Control of a Hybrid Renewable
Energy Production Unit, ICGST-ACSE Journal, Vol. 7, No. 1, pp.
1-9, May 2007.
Meenakshisundaram Arutchelvi, Samuel Arul Daniel, Grid
connected hybrid dispersed power generators based on PV array
and wind–driven Induction Generator, Journal of electrical
engineering, Vol. 60, No. 6, pp. 313–320, 2009.
Kiran K. Chedella, Lin Zhao and Zhihao Zhang, Modeling of a
Small Stand-Alone AC System with the Dynamic Models of Fuel
Cells and Solar Panels, ESA Annual Meeting on Electrostatics,
2010.
Majid Nayeripour, Mohammad Hoseintabar, A Comprehesnive
Dynamic Modeling of Grid Connected Hybrid Renewable Power
Generation and Storage System, International Journal of
Modeling and Optimization, Vol. 1, Issue 1, pp. 62-65, Apr. 2011.
Tamer T.N. Khatib, Azah Mohamed, Nowshad Amin, A New
Controller Scheme for Photovoltaics Power Generation Systems,
European Journal of Scientific Research, Vol.33, No.3, pp.515524, 2009.
Thanaa F. El-Shatter, Mona N. Eskander, Mohsen T. El-Hagry,
Energy flow and management of a hybrid wind/PV/fuel cell
generation system, Energy Conversion and Management, Vol. 47,
pp. 1264–1280, 2006.
Yuen-Haw Chang and Chia-Yu Chang, A Maximum Power Point
Tracking of PV System by Scaling Fuzzy Control, International
multiconference of Engineers and Computer Scientists (IMECS
2010), Hong Kong, Vol.2, Mar.2010.
Chun-Yao Lee, Yi-Xing Shen, Jung-Cheng Cheng, Chih-Wen
Chang and Yi-Yin Li, Optimization Method Based MPPT for
Wind Power Generators, World Academy of Science, Engineering
and Technology, pp. 169-172, 2009.
Eftichios Koutroulis and Kostas Kalaitzakis, “Design of a
Maximum Power Tracking System for Wind-Energy-Conversion
Applications”, IEEE Transactions on Industrial Electronics, Vol.
53, No. 2, pp. 486-494, April – 2006.
Whei-Min Lin, Chih-Ming Hong, Fu-Sheng Cheng, and Kai Hung
Lu, MPPT Control Strategy for Wind Energy Conversion System
Based on RBF Network, IEEE Conference Energytech, 2011, pp.
1-6.
S. Chandra Has, P.Ajay-D-Vimalraj, G.R.K.D.Satya Prasad,
Power Enhancement of Wind Energy Conversion System Using
Hybrid Method, International Journal of Advanced Engineering
Sciences and Technologies, Vol. No. 7, No. 2, pp. 248 – 253,
2011.
Meo, S., Perfetto, A., Esposito, F., Fuel-cell based inverter for
residential systems, 5th IASTED International Conference on
POWER AND ENERGY SYSTEMS (EuroPES 2005), June 15-17,
2005, Benalmádena, Spain, art. no. 468-149, pp. 49-54.
Meo, S., Pagano, M., Paparo, G., Velotto, G., Integration of fuel
cell electrical source in renewable energy power system supply,
(2004) International Conference on Probabilistic Methods
Applied to Power Systems, September 12-16, Ames, Iowa, Iowa
State University, pp. 445-450.
Meo, S., Perfetto, A., Piegari, L., Esposito, F., A ZVS current fed
dc/dc converter oriented for applications fuel-cell-based, (2004)
IECON Proceedings (Industrial Electronics Conference), 1, 30th
Annual Conference of the IEEE Industrial Electronics Society,
November 2 - 6, 2004, Busan, Korea, art. no. TA7-71, pp. 932937.
Authors’ information
T. Bogaraj completed his BE degree from
Government
College
of
Technology,
Coimbatore during 1995. He worked as
maintenance engineer for three years in a Paper
Industry. Then he received M.Tech. degree in
Process Control and Instrumentation from
National Institute of Technology, Trichirapalli,
Tamilnadu, India. Since 2003 he is working as
Assistant Professor in Electrical and Electronics Department, PSG
College of Technology, Coimbatore, India. His fields of interests are
Control systems, Renewable energy systems, Hybrid power systems,
Virtual Instrumentation.
Dr. J. Kanakaraj received his B.E degree in
Electrical and Electronics Engineering from
Coimbatore
Institute
of
Technology,
Coimbatore, Tamil Nadu, India and M.E.
Degree in Electrical Machines from PSG
College of Technology, Coimbatore, Tamil
Nadu, India. He received his Ph.D in Electrical
and Electronics Engineering from Bharathiar
Univerity, Coimbatore, Tamil Nadu, India. He is currently working as
Associate Professor in Electrical and Electronics Department, at PSG
College of Technology, Coimbatore, India. His research interests
include in the areas of Control systems, Measurements and Control
systems, Special Electrical machines, and Renewable energy systems.
He has published several International and National Journals.
Copyright © 2012 Praise Worthy Prize S.r.l. - All rights reserved
International Review on Modelling and Simulations, Vol. 5, N. 4
1709
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