2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation
Dynamic Efficiency of Direct Control Based Maximum Power Point Trackers
Kashif Ishaque1 and Zainal Salam2
1
Karachi Institute of Economics and Technology, Karachi 75190, Pakistan.
Faculty of Electrical Engineering, Universiti Teknologi Malaysia, UTM 81310, Skudai,
Johor Bahru, Malaysia.
kashif@fkegraduate.utm.my, zainals@fke.utm.my
2
operates in conjunction with a dc-dc power converter, whose
duty cycle is adjusted to suitably track the MPP of the PV
array. However, because of the non-linear I–V characteristics
of the PV curve and the consequence of the varying
environmental conditions, tracking of correct maximum
power point (MPP) can sometimes be a challenging task.
As a result, several MPPT schemes have been proposed
[6-16]; among the more popular ones are the perturb and
observe (P&O) [6], incremental conductance (IC) [7, 8],
ripple correlation [9], short circuit current [10] and opencircuit voltage approaches [11]. Many of these, particularly
the first two, are the most commonly implemented methods
in existing PV systems. The P&O is based on the
perturbation in the voltage/current/duty cycle of the
associated power converter while IC works by incrementally
comparing the ratio of derivative of conductance with the
instantaneous conductance. More recently, various
researcher employed the soft computing techniques for
MPPT rationale that include fuzzy logic control [12], neural
network [13] and evolutionary algorithms[14-16].
It is interesting to note that in most of the
aforementioned works, the results are validated using static
efficiency analyses i.e. the ability of the tracker to find and
hold on to the MPP once the insolation and temperature
conditions are steady. Although, such approach is quite
satisfactory when dealing with certain environmental
variations which are almost static in nature, the performance
of these methods under dynamic weather conditions could
not be sufficiently quantified. This situation is more severe
in the tropical regions like Malaysia, in which the weather
fluctuations is very frequent and thus the MPPT have to be
robust to ensure the optimal yield from the PV source. Thus,
an alternative efficiency computational method, the
European Standard for the Efficiency Test, can be
considered as a more suitable and realistic approach to
quantify the performance of inverter in such conditions. The
standard is released in the form of the EN 50530 document
[17]. The test is more stringent and has rapidly becomes the
benchmark test to validate the MPPT performance of grid
connected inverters for evaluating the steady state and
dynamic efficiency.
On the aforementioned basis, this work is carried out to
compare the efficiency performance of the two well accepted
MPPT techniques i.e. P&O and IC when tested using the EN
50530 test. Both methods are widely used in existing PV
Abstract— Due to stringent standards for efficiency of
maximum power point tracker (MPPT) of photovoltaic (PV)
system, the performance of MPPT in PV system under dynamic
environmental conditions is very crucial. Thus, this paper
evaluates the performance of perturb & observe (P&O) and
incremental conductance (IC) MPPT techniques using the
European Efficiency Test, EN 50530, which is specifically
devised for the dynamic performance of PV system. Both
MPPT techniques are implemented in direct control structure
in conjunction with a buck-boost converter, which is used for
tracking device. Experiments are conducted using a custom
designed PV array simulator. Results reveal that both methods
yield almost equivalent MPPT efficiency; however, in average,
the performance of IC method is found to be slightly better that
gives 98.5% efficiency compare to 98.3% in P&O. It is also seen
that the performance of IC method is very sensitive to its
perturbation size, especially at low insolation levels.
Keywords— Direct Control, dynamic efficiency; incremental
conductance (IC); maximum power point tracker (MPPT);
perturb and observe (P&O); photovoltaic (PV) system.
I.
INTRODUCTION
Efficiency of a PV system is mainly influenced by three
factors: (1) the efficiency of the PV cell, which depending
on the technology, is about 8-15% [1], (2) the efficiency of
the power converters (dc-dc and/or dc-ac) which is normally
ranges between 95-98 % [2] and (3) the efficiency of the
maximum power point tracking (MPPT) algorithm, which in
most of the cases can go beyond 98% [3]. There are also
looses due to cabling, mismatch between the array and the
load/battery and energy loss in batteries [4]. To increase the
efficiency of the cell, improved fabrication technologies and
designs at silicon level is required [5], which is usually
performed by large PV companies or well-established
research institutions. Improvements in the converter
topology and design can also result in improved efficiency;
however, in certain cases, it can significantly increase the
overall cost of the installed PV system.
Alternatively, improving the MPPT algorithm is much
easier, less expensive and can be retrofitted in existing PV
system plants. The aim of MPPT is to ensure that at any
environmental condition (particularly solar insolation and
temperature), the maximum power is extracted from the PV
modules by matching its I–V operating point with the
corresponding power converter. Generally, a PV array
2166-0662/14 $31.00 © 2014 IEEE
DOI 10.1109/ISMS.2014.79
429
direct duty cycle MPPT control. In this scheme, the PI block
in Fig. 1 is eliminated and duty cycle is computed directly in
the MPPT algorithm.
system; hence their performance under rapid changes of
weather will give a clear picture about the robustness of
these MPPT techniques. To implement these practically, a
dc-dc buck-boost converter is configured as MPPT in a
direct control structure. Both methods are implemented using
a Dspace DS1104 [18] Digital Signal Processor (DSP) which
is explicitly configured to optimize the control actions.
Although the test is geared for inverters, it can be readily
used for DC-DC converters because the main aim is to
evaluate the performance of the MPPT algorithm, rather than
conversion efficiency itself [17].
The remainder of the paper is organized as follows: the
next section discusses about the background knowledge of
EN50530 test. Section 3 describes the MPPT structure of
both methods. This is followed by a brief overview of P&O
and IC techniques. While in section 5, the experimental
setup of the whole efficiency test is shown. Tracking results
of EN50530 test are discussed in the next section. Finally,
the conclusions are made in the last section.
PV Array
IPV
CPV
MPPT
*
VPV* /IPV
Fixed or
Adaptive
VPV/IPV
DC/DC
Converer
III.
L
O
A
D
VPV
Duty
Cycled
PI
q
switching
signal
Fig. 1 A typical voltage/current based MPPT system
II.
THE EN50530 TEST SEQUENCE
The dynamic test EN50530 standard defines a test
procedure for the measurement of MPPT efficiency of the
grid-connected PV systems with the help of a PV array
simulator that simulates the output characteristics of a PV
source. Although the test is geared for inverters, it can be
readily used for DC-DC converters because the main aim is
to evaluate the performance of the MPPT algorithm, rather
than conversion efficiency itself [17].
The dynamic MPPT efficiency test under rapid changes of
weather conditions is characterized by the combination of
various ramp profiles over a certain span of time. For an
entire time range, three different types of insolation regions
are used to characterize three types of insolation levels based
on a reference value of 1000 W/m² i.e. (1) Low to medium
insolation (100–500 W/m²), (2) medium to high insolation
(300–1000 W/m²) and (3) startup and shut down insolation
(2–100 W/m²). For the case of (1) and (2), the sequences of
ramp having slopes ranging from 0.5 W/m²/s up to 100
W/m²/s are utilized. This is followed by a single ramp profile
with a slope of 0.1 W/m²/s for startup and shut down
condition.
Figure 2 shows the complete dynamic test profile for the
EN50530 standard that is used to evaluate the effectiveness
of any MPPT method. As stated earlier that, since in this
work only dc-dc converter is used, it is not necessary to use
the test sequence startup and shut down region as shown in
Fig. 2. Thus, sequences (1) and (2) will be used to evaluate
the performance of P&O and IC method. Sequence (3), i.e.
for startup and shut down insolation is omitted.
DIRECT CONTROL STRUCTURE OF MPPT
The aim of employing MPPT is to ensure that at any
environmental condition (particularly solar insolation and
temperature), maximum power is extracted from the PV
modules. This is achieved by matching the PV’s MPP with
the corresponding power converter’s operating voltage and
current. Fig. 1 shows the general block diagram of MPPT in
conjunction with a dc-dc converter. Although a stand-alone
dc-dc system is depicted here, the application can be
extended to a grid connected PV system by adding other
power electronic devices such as inverter and grid
components. Basically, the MPPT works by sensing the
current and voltage of the PV array; using this information
the array power is calculated and compared with the present
value of MPP. Accordingly, the duty cycle of the converter
is adjusted using a PI or hysteresis controller to match the
MPP– which in turn, forces the converter to extract the
maximum power from the array. An alternative control
structure is characterized by directly updating the duty cycle
of the power converter; this is known in literature as the
Fig. 2 The complete dynamic test profile
IV.
BRIEF OVERVIEW OF MPPT TECHNIQUES
A. Perturb and Observe (P&O)
The working principle of P&O is depicted by the
flowchart in Fig. 3. The algorithm introduces a perturbation
(Φ) in the duty cycle of power converter that changes the
operating voltage and current of PV array and subsequently
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is based on duty cycle, d*. Another alternative yet effective
way to utilize the IC was proposed by number of researchers
[19]. The idea is to generate a marginal error ε using the
instantaneous conductance and the incremental conductance.
Mathematically, it can be written as:
the change in the operating power is observed. The
increment in operating power implies that the converter is
getting closer to the MPP. Accordingly, in the next sampling
cycle, the direction (slope) of perturbation is retained and the
reference duty cycle is further increased by an amount Φ.
dI
I
dV V
(4)
From (4), it can be seen that the value of ε is zero at
MPP. Therefore, based on the amount of ε, the same rules of
IC using (3) can be used. The value of ε is usually selected
based on the tradeoff between the accuracy of (4) and
oscillations around MPP. Furthermore, this value should be
less than perturbation step Φ. It can be noted that (2) rarely
exists in practical application; hence selecting a suitable
value of ε eases the MPPT algorithm. The basic flow chart
for IC is depicted in Fig. 4. It can be noticed that the
reference signal is based on duty cycle, d*.
Fig. 3 Basic flow chart of Perturb and Observe (P&O)
B. Incremental Conductance (IC)
The idea behind IC is to incrementally compare the ratio
of derivative of conductance with the instantaneous
conductance [1]. It is derived from the fact that at MPP, the
derivative of power with respect to voltage (dP/dV) is in fact
zero, i.e.
dP d (VI )
dI
I V
0
dV
dV
dV
(1)
Equation (1) can be rearranged in the following form:
I
dI
I
V dV V
(2)
where ΔI and ΔV are the increments of PV current and
voltage, respectively. The basic rules for IC can thus be
derived from the basic P–V characteristics (not shown here
for brevity) and can be written as:
I
dI
dV V , at MPP
I
dI
, Left of MPP
V
dV
I
dI
dV V , Right of MPP
Fig. 4 Basic flow chart of Incremental Conductance (IC)
V.
(3)
EXPERIMENTAL SETUP
An overall block diagram of the experimental set-up to
compute the MPPT efficiency using the EN 50530 standard
is shown in Fig. 5 (a). The photograph of the experimental
rig is depicted in Fig. 5 (b). In this work, the buck–boost
topology is designed for continuous inductor current mode
with the following specifications: C1 = 470 μF, C2 = 220 μF,
L = 1 mH, and 50-kHz switching frequency.
Using the rules in (3), the basic flow chart for IC is
depicted in Fig. 4. It can be noticed that the reference signal
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PVAS
IPV
DC/DC
Buck-Boost
Converter
CPV
VPV
Digital ground
Power ground
L
O
A
D
Gate
driver
Voltage
Transducer
DS1104
Current
Transducer
PCI
PC
(b)
(a)
Fig.5 (a) Overall block diagram of the experimental set-up (b) experimental rig
A resistive load of 10Ω is connected at the output of
MPPT converter. To ensure that system attains the steady state
before another MPPT cycle is initiated, the sampling time is
chosen as 50ms.
To realize the EN 50350 insolation sequence, the test are
conducted using a custom-designed PV Array Simulator, the
PVAS [20]. The simulator consists of a linear, high-voltage
MOSFET based power stage and current controller which is
controlled by an integrated I–V curve generation unit based on
a voltage controlled current source concept. The I–V curve
generation unit consists of an integrated RAM, which holds
the I–V curves and a microprocessor unit that provides
complete communication, monitoring and measurement
capabilities.
The PV array configuration that is used to emulate the
actual I–V curves is comprised of one string, having four
series-connected modules. Each module is rated at STC as
follows: PMAX = 60 W, IMPP = 3.5 A, VMPP = 17.1 V, ISC = 3.8
A and VOC = 21.1 V. Both P&O and IC methods are
implemented using the flow chart as given in Fig. 3 and 4,
with a fixed step perturbation (Φ) of 0.01. This value is
selected based on the tradeoff between tracking speed and
steady state MPP oscillations. Additionally, the marginal
value of error ε for the IC method is chosen to be 0.002 which
is significantly less than perturbation size and thus, it will not
interrupt the MPPT function.
VI.
as the initial setting time is imposed on the PV array, as
shown in the second zoomed view in Fig. 6. This is
introduced to make MPPT converter in stable condition and
for that reason, these uniform sectors are accounted for
dynamic efficiency computations.
It can be seen in Fig. 6 that P&O method adequately tracks
the ideal MPP power for the whole dynamic profile (ramps
sequence). However, a consistent deviation between the
dotted line (ideal MPP power) and solid line (actual PV
power) is clearly visible, as shown in the zoomed view 1. This
implies that P&O method with a fixed perturbation size (Φ) of
0.01 is slower in nature despite a significantly less MPPT
sampling time of 0.05s. This is due to the slower dynamics of
hill climbing nature of P&O method that operates in open
loop control fashion. Besides this, due to the fixed value of Φ
in the MPPT algorithm, the oscillations at the steady state are
constantly appearing that also deteriorating the dynamic
performance of P&O. In average, this tracking difference
between the dotted and solid line results in an approximately
1.5% MPP error in the tracked power.
RESULTS AND DISCUSSION
Figure 6 shows the results of ideal array MPP variables
(power and voltage) and measured MPP variables obtained by
P&O method for dynamic test sequence in Fig. 2. The ideal
power and voltage is the calculated PV power and voltage
based on the applied insolation on solar array, which is
computed inside the PVAS software. These measured
variables are obtained using the data acquisition system of
PVAS and accordingly analyzed using the Matlab tools. By
observing the obtained results, it can be seen that the complete
set of results of Fig. 6 are comprised of 5 hours and 15
minutes. To start a new ramp sequence in Fig. 6, a uniform
value of insolation for a certain period (60s), which is known
Fig. 6 Performance of P&O method under dynamic test conditions
Figure 7 shows the results of IC technique for the same
dynamic profile of Fig. 5. Similar to P&O, the IC method is also
tracking the ideal MPP power well enough for the whole
432
TABLE I DETAILED PERFORMANCE OF P&O AND IC METHODS UNDER
DYNAMIC WEATHER CONDITIONS
insolation profile. But, zoomed view (of the dotted circle in Fig.
7), shows the fair evidence that IC method too does not closely
follow the PV power imposed by the EN 50530; as the tracking
error between the dotted line and solid line is truly visible.
However, the average performance of IC method is just better
than P&O (will be shown later in Table 1). Nevertheless, the
oscillatory behavior of IC in Fig. 7 confirms the fact that this
method also suffers with the steady state fluctuations, as
reported in several works [5, 9].
Number of ramps
Slope
Duration
in per slop
(W/m2/s)
(s)
Low to medium insolation region
2
0.5
3240
2
1
1640
3
2
1260
4
3
1147
6
5
1080
8
7
1074
10
10
1000
10
14
771
10
20
600
10
30
467
10
50
360
Medium to high insolation region
10
10
1600
10
14
1200
10
20
900
10
30
667
10
50
480
10
100
340
ƞMPPT
P&O
ƞMPPT
IC
98.10
98.25
98.18
98.22
98.18
98.16
98.20
98.17
98.18
98.16
98.03
98.39
98.37
98.33
98.37
98.35
98.32
98.31
98.33
98.08
98.34
98.30
98.39
98.45
98.44
98.42
98.35
98.28
98.59
98.58
98.60
98.56
98.56
98.50
ACKNOWLEDGMENT
The authors would like to thank Karachi Institute of
Economics and Technology (KIET) and Inverter Quality
Control Centre (IQCC) of Universiti Teknologi Malaysia
(UTM) for providing the research facilities to conduct this
research.
Fig. 7 Performance of IC method under dynamic test conditions
The detailed dynamic performance (only the ramps portion)
of both methods is given in Table 1. The results are tabularized
with respect to different slopes in the complete profile. It can be
seen that under low to medium insolation region, the average
dynamic efficiency of P&O and IC method is found to be
98.17% and 98.32%, respectively. The performance of IC
method is somehow better than P&O; especially, at higher
slopes, where the resulting efficiency of IC is 98.30% to that of
98% in P&O. This once again highlights the fact that a fixed
step P&O is very slow to react for the variable weather
conditions. From the obtained results, it is interesting to note
that the performance of both methods under medium to high
insolation region is slightly improved. The average efficiency in
this case is found to be 98.4% and 98.6% for P&O and IC,
respectively. The better performance of both approaches can be
attributed to its perturbation size Φ that gives better results at
higher current.
VII.
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CONCLUSION
In this work, the performance of two well known MPPT
techniques i.e. P&O and IC method is tested under dynamic
environmental conditions. The obtained results have shown
that both P&O and IC method in their fixed step perturbation
structure does not yield better efficiency. The IC method has
slightly better average performance and results 98.5%
efficiency compare to 98.3% in P&O. The performance of
each method is deteriorated at low insolation levels, especially
IC method that on many occasions gives efficiency less than
95%. However, the performance of both methods can be
improved by employing the adaptive perturbation size in their
respective MPPT structure.
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Kashif Ishaque received his B.E. in Industrial
Electronics Engineering from Institute of Industrial
Electronics Engineering, NEDUET, Karachi,
Pakistan, in 2007, Master of Engineering science
and PhD from Universiti Teknologi Malaysia in
2009 and 2012 respectively. He is currently an
assistant professor at Department of Electronics
Engineering; PAF Karachi Institute of Economics
and Technology (PAF-KIET), Karachi, Pakistan.
Dr. Kashif is the author and co-author of around 40
publications in international journals and proceedings. His research interests
include photovoltaic modelling and control, intelligent control, nonlinear
systems control and optimization techniques such as genetic algorithm (GA),
particle swarm optimization (PSO) and differential evolution (DE).
Zainal Salam Zainal Salam obtained his B.S., M.E. and Ph.D. from the
University of California, the Universiti Teknologi Malaysia (UTM) and the
University of Birmingham, UK, in 1985, 1989 and
1997, respectively. He has been a lecturer at UTM for
24 years and is now a Professor of Power Electronics
in the School of Electrical Engineering. He has been
working on several research and consulting projects in
the area of battery powered converters. Currently he is
the Director of the Inverter Quality Control Center
(IQCC) at UTM which is responsible for testing PV
inverters that are to be connected to the local utility
grid. His research interests include all areas of power electronics, renewable
energy, power electronics and machine control.
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