A Review on Maximum Power Point Tracking Techniques under Partial Shading Condition

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 5, July-August 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD50590 | Volume – 6 | Issue – 5 | July-August 2022 Page 919
A Review on Maximum Power Point Tracking
Techniques under Partial Shading Condition
Rishi Kumar Gupta1
, Nita Vishwakarma2
1
Student, 2
Assistant Professor,
1,2
Oriental Institute of Science & Technology, Bhopal, Madhya Pradesh, India
ABSTRACT
Partially shaded condition (PSC) is one of the major problems in
large photovoltaic generation systems. It causes losses in output
power and hot spot effects. Under PSC, PV characteristic curve
exhibits multiple peaks having one global maximum power point and
multiple local maximum power points. Tracking the global maximum
power point is one of the main challenges the design engineers have
to face. The paper presents the recent work done on the development
of Global Maximum Power Point Tracking (GMPPT) algorithms
under partial shading condition and their comparative analysis. To
have focus on GMPPT techniques used in PSC, traditional MPPT
techniques that cannot distinguish GMPP from local maximum
power points have not been discussed.
KEYWORDS: Partial Shading Condition (PSC), Global Maximum
Power Point Tracking (GMPPT), P-V characteristics
How to cite this paper: Rishi Kumar
Gupta | Nita Vishwakarma "A Review
on Maximum Power Point Tracking
Techniques under Partial Shading
Condition"
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I. INTRODUCTION
The problem of partial shading in the area of solar
photovoltaic systems (SPV) has generated interest in
the research community since 20 years. Partial
shading occurs when the modules connected in series
and parallel doesn’t receive same illumination and the
result is different power generation by the different
modules for the same rating of the panel. And if same
power doesn’t flow through all the modules, the
modules which are generating lower power will act as
sink and the power will be absorbed from the
modules which are generating more power leading to
hotspot formation and consequent irrelevant damage
of the PV module. To overcome this problem diodes
are generally connected in parallel with the PV panel.
Some manufacturers provide inbuilt bypass diodes
with the PV module. Normally bypass diodes are
provided in parallel with series connected PV cells as
shown in figure 1. But it introduces complexity in the
nonlinear PV characteristic curve with multiple
maxima under PSC. Even large PV plants are being
built in a fixed series- parallel configuration and
modules have bypass diode included in different
configurations. To extract maximum power from the
PV system MPPT is employed [1].
Various review papers for MPPT methods are
available for solar PV power generating systems till
date. Some papers have discussed both traditional
methods as well as those suitable for partial shading
condition (PSC) [2-5]. Some have discussed only
MPPT methods for partial shading condition [6-8].
For further optimization of the PV system, partial
shading has to be taken into consideration by the
researchers. In this paper a comprehensive review of
the papers on maximum power point tracking under
PSC has been presented. A comparative analysis
based on the advantages and shortcomings has also
been included.
II. PARTIAL SHADED CONDITION(PSC)
PROBLEM
To enhance the power handling capacity of solar PV
generating systems, the solar PV modules are
connected in series (to enhance voltage level) and/or
parallel (to enhance current level) with each other.
IJTSRD50590

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
Under uniform solar insolation on these modules the
complete PV generating unit has a unique maximum
power point that can be tracked using various gradient
search based conventional methods. But under
different insolation (due to clouding, tree or building
shade etc.) the IV characteristics in series connected
modules differ resulting in mismatch in the operating
point. This causes some of the modules which are
heavily shaded to act as load consuming power
thereby giving rise to a situation called hotspot
formation as shown in figure 1 which ultimately
results in irreversibly damage of the module. To
overcome this problem bypass diodes are connected
across the module. The current thus bypasses the
shaded module. Figure 1 shows a PV array composed
of three modules in a string and its characteristic
curves under uniform insolation and partial shading
condition (with and without bypass diode). Two local
MPPs can be seen on the PV characteristic curve
other than the GMPP (global maximum power point).
The popular gradient search or hill climbing method
employing the logic of finding zero gradient
(dp/dv=0) fails as the algorithm sticks and start
operating in local maximum power point. Thus an
efficient algorithm to track global maxima under PSC
is required.
Fig1. (a) PV array under uniform insolation. (b) PV array under PSC.
(c) PV characteristic for (a) and (b). (d) IV characteristic for (a) and (b).

III. GLOBAL MAXIMUM POWER POINT TRACKING TECHNIQUES UNDER PSC
Recent algorithms for Global MPP tracking can be classified as follows.
1. Modified conventional MPPT
2. Utilizing the features of the characteristic PV curve
3. Metaheuristic nature inspired methods
1. Modified conventional MPPT
1. Two stage searching method: Search for the GMPP location interval is done in the first stage and then by
using traditional MPPT methods in the second stage precise GMPP location is found out [6]. H. Patel and V.
Agarwal [9] used this method with the basic search rules. They took 0.85 Voc, all (total O.C voltage of the
system) as the P&O search starting point. Then they used the peak value found in step 1 as the basis to move
the operating point one large step to the left. If the peak value obtained was greater than the previous one,
step 2 was repeated, if it was smaller, then previous peak value was considered GMPP.
Advantage of this method is that its implementation is easy and it can be integrated into conventional Power
Generating System. Its disadvantages are that it can fail to track GMPP in some cases. It is successful in cases
when we move from start or from VOC (as shown in figure 2). Also probability of the GMPP being tracked is
dependent on value of the large step. Its tracking speed is also less as maximum power point of each curve has to
be obtained using P&O method. Works in [10-12] has similar methods. Authors employed a large interval for
the entire P–V characteristic curve and determined the largest peak value. Then a refined search is done near this
peak value to locate the GMPP. Flowchart of the method is given in figure 3.
2. System characteristic curve method: Works in [13-15] used a preset function (linear) to move the operating
point near the GMPP. Flowchart in figure 4 describes the principle of this method. The linear function
depends on various system parameters such as O.C voltage and S.C current. The tracking speed reported in
the work is quite fast. But for obtaining O.C voltage and S.C current, open or short circuits are required
which can lead to power loss or safety concerns. Moreover, this method fails under complex shading pattern.
3. Current sweeping method: In this method maximum power point is tracked using PV output current instead
of PV output voltage. Most of the MPPT algorithms adjust the duty ratio of the DC to DC converter which
indirectly adjusts the output voltage. However, changing the duty ratio affects both the output voltage and
current. By utilizing the dynamics of the DC to DC converter, Tsang and Chan [16] used current sweeping
method to develop a good current controller for the DC to DC converter by having a firm control on the
output current. Then a prompt current sweeping signal can be sent for the converter making it possible to
track the GMPP very quickly. Authors have included simulation & experimental results. The performance
reported is satisfactory.
4. Distributed MPPT (DMPPT): The output of every PV module has its own DC to DC converter in this
method. These DC to DC converters operate at low power levels since they supply only the equalization
current. Thus power losses are significantly reduced [17-18]. For multiple strings in parallel without the loss
of their MPPs, this current equalization topology is extended as shunt-series compensation in [19]. Each
output of the DC to DC converter is connected in series to form the PV string. These strings are connected at
the output of every module. Every DC to DC converter processes the whole power produced by the
corresponding modules and then tracks the MPP for that individual module. The total available MPP power
of the array is somewhat increased. But as separate DC to DC converter is there for every module, its
implementation becomes complex.
5. Electrical PV array Reconfiguration: Works in [20] and
[21] showed that immediate adjustment of the PV array configuration with respect to the pattern in shading can
reduce the power losses caused by the partial shading condition. Although reconfiguration method can minimize
effects of PSC, it requires use of a switching matrix to effect the changes in architecture. Therefore, the system
becomes costly and the complexity of the controller design also increases. Moreover, reconfiguration technique
can fail to track GMPP in some shading patterns.
2. Utilizing the features of the characteristic PV curve
1. Direct method: P–V characteristic curve resembles Lipschitz characteristics and hence peak values can be
found by dividing rectangle (DIRECT) technique [22]. Here “direct” means to divide the searching area into
three different areas with equal intervals and to find potentially optimal intervals using mathematical
equations. Its merit is that it has a strong mathematical foundation and has good tracking speed. Its demerit is

that it fails to track the GMPP under some shading pattern, and this method cannot be integrated directlyinto
conventional power generating system. Flowchart of the method is shown in figure 5.
3. Fibonacci methods: Fibonacci sequence which is used as the mathematical basis for segmentation is the main
difference between Fibonacci method and DIRECT method. The Fibonacci search method search for a
sorted array by using an algorithm of divide and conquer, narrowing down possible locations using
Fibonacci numbers. It continuously narrows down the range having the optimal point always within that
range. It has strong mathematical foundation and good tracking speed. Its disadvantage is that it fails to track
the GMPP under some shading pattern, and it cannot be integrated directly into conventional power
generating system. A random number method is used in [23] where power values have been randomly
sampled under six well defined voltages, and then the new search range is obtained using that point which
has the highest power value. Repeated random sampling was done with new search range for getting
convergence.
3. Metaheuristic nature inspired methods
1. Fuzzy Logic Control (FLC): To understand the system to be controlled, traditional control system design
uses accurate mathematical models for describing the system. But when the system to be controlled
becomes very complex, system identification method cannot establish a system model. However, FLC
converts the linguistic values into automatic control action with the help of expert knowledge using the
fuzzy set theory. Its advantages are that there is no need of precise mathematical model. It is also suitable
for systems which are nonlinear and vary with time. Systems whose complete model is unknown can also be
dealt with this method. Therefore PSC (partially shading condition) problems can be dealt with FLC.
Karatepe et al. [24] utilized FLC in place of traditional MPPT methods. Since each converter has an MPPT
controller, tracking of GMPP is guaranteed.
2. Ant colony optimization: It is one of the evolutionary computation methods and has been widely used in
image processing [26], scheduling [27], power electronic circuit design [28] and many other fields. Its main
advantage is that it adjusts the command values very fast according to environmental changes. Thus, this
technique is suitable to track MPP under varying environmental condition. In this method a random path is
selected by each agent at first. If the path chosen is short, the agent drops concentrated pheromone on that
path. Then in the next iteration, the path is chosen on the basis of the concentration of pheromone on that
path. The probability of the path to be chosen by the agent depends on the concentration of the pheromone.
Jiang et al. [29] used this method to develop MPPT control scheme for PV systems under partial shading
condition. Single current and voltage sensors were used which simplified the system and reduced its cost. It
also provides fast convergence independent of the initial condition. But only the simulation results were
provided in [29].
3. Differential Evolution (DE): It is a population based, stochastic evolutionary algorithm [30]. It differs from
GA in the context that it relies on mutation process rather than crossover. Mutation operation is used as a
search mechanism and selection operation for directing the search towards the prospective regions of the

search space. This method has three advantages (1) locates accurate GMPP without depending on the initial
values taken (2) convergence is fast (3) utilizes few control parameters, hence easy to use. [30] and [31]
showed simulation results based on DE.
4. Particle swarm optimization (PSO): Among the various Evolutionary Algorithm techniques, PSO has one of
the simplest structures that can be used to track MPP under PSC. The main advantage of the method
proposed in [32] is the use of direct duty cycle control method by removing PI control loops. The PSO
algorithm proposed in [33] took about 1 to 2 seconds to find the GMPP. More importantly, the response
time was almost independent of the search space dimensions taken and partial shading pattern.
5. Chaos search (CS) method: It is a stochastic search method which uses chaos theory as its basis. Its search
results are much better than those of the search methods which use pure random numbers. In [34] control
variables are randomly produced using dual carrier with the help of this method. Then output power is
measured using the control variables which is used to find the GMPP. According to [34], dual- carrier
Chaos Search can accurately track the GMPP under PSC, the search efficiency is improved, has good
precision and system robustness and has a simple control mode.
6. Simulated Annealing method: In [35-36] a simulated annealing-based global MPPT method designed for
PSC has been proposed. It is based on following the heating and cooling processes in metals to find the
global optimum solution. The energy measured during the heating and cooling process is compared to the
ongoing state and if the new operating point has more energy, the new working point is selected as the new
one. The algorithm achieves objective but with some increment in computational complexity as compared to
the P&O technique and it has also fewer parameters stored in the memory than the PSO technique. Starting
value of the algorithm is not an issue for tracking the GMPP.
7. Grey- Wolf optimization: The Grey-Wolf optimization (GWO) method is proposed in [37-38] as an
algorithm that overcomes problems such as steady-state oscillations, lower tracking efficiency, which have
been seen in P&O and PSO methods. This method detects the shading pattern variations and is faster to
converge to the global maximum, and has less steady-state oscillations. This algorithm has some
disadvantages also. It has a complex initialization part and there are lots of unknown parameters which have
to be determined by the designer himself.
8. Artificial bee colony algorithm: An artificial bee colony (ABC) algorithm for global MPP has been
proposed in [39- 40]. The proposed method takes less tracking time for GMPP, compared with PSO and
enhanced P&O (EPO). It has fewer control parameters and its convergence doesn’t depend on initial
conditions. The comparison results indicate that, ABC method is slightly better than PSO and EPO in
efficiency, convergence and GMPP time parameters. But the implementation complexity of this method is
quite high as compared to the other methods.

9. Cuckoo Search (CS) algorithm: It is an optimization algorithm based on parasitic reproduction strategies of
cuckoo birds [41]. Yang and Deb introduced this method in 2009 [42]. Several birds of cuckoo family
perform brood parasitism, i.e. Firstly, each cuckoo lays one egg in every iteration, and then a nest is
randomly chosen by it to lay its egg in. Secondly, the best nest having the top quality solution will be
carried forward to the next generation. Thirdly, there are fixed number of host nests and the probability of
alien eggs being discovered by a host bird is pa ∈ [0, 1]. It has several advantages such as higher efficiency,
fast convergence and fewer tuning parameters [41]. Among them the most pronouncing is the dependence
of PS size on random numbers. Its flowchart is shown in figure 7.
IV. CONCLUSION
Recent work on the topic of GMPPT tracking suggests it to be a popular & hot area of research. In this study
various GMPPT methods have been discussed with their merits and demerits from diverse references. Table I
sums up the advantages and disadvantages of these methods reported in literature.
Table I
Classification Methods Advantages Disadvantages
Modified
conventional
MPPT
System
characteristic curve
method
Good tracking speed
Requirement of open or short
circuits can cause power loss or
safety concerns,
method fails in some cases
Two stage
searching method
Its implementation is easy
and it can be integrated into
traditional PGS
It can fail to track GMPP in
some cases
Current sweeping
method
Fast tracking speed
Requires periodical tracking of
the MPP
Electrical PV array
Reconfiguration
Compensate the power losses
caused by PSC
Expensive and the controller
design is also complex, fail to
track GMPP in some
shading patterns
Distributed MPPT
(DMPPT)
total available MPP power of
the PV array is increased
Implementation complexity is
high
Methods based
on utilizing the
features of the
characteristic
PV curve
Direct method
Based on a strong
mathematical foundation and
good tracking speed
Cannot be directly integrated
into conventional PGS
Fibonacci methods
Based on a strong
mathematical foundation
Fail to track GMPP in some
cases and cannot be directly
integrated into conventional
PGS
Metaheuristic
nature
inspired
methods
Fuzzy logic control
No need of precise
mathematical model, it is very
suitable for use in non-linear,
time-varying and systems
without complete models
high hardware cost
Genetic Algorithm
Can optimize parameters of
other algorithms such as FLC
Its implementation is complex
and difficult to achieve using
low cost microcontroller
Ant colony
optimization
Fast convergence and
convergence independent of
the initial condition
Implementation is difficult
Differential
Evolution
Fast convergence and
convergence independent of
the initial condition, easy to
use
Some parameters may not
guarantee optimal solution
Particle swarm
optimization
Simpler structure than other
EA techniques
Optimization performance
depends on parameter selection

Chaos search
method
Improved search efficiency,
precision, and system
robustness
High complexity
Simulated
annealing
Requires fewer parameters to
be stored, independent of
initial condition
Slight more complex
Grey- Wolf
optimization
Less oscillations, high
efficiency, high speed
complex initialization part, more
unknown parameters
Artificial bee
colony (ABC)
algorithm
Less tracking time, fewer
control parameters,
independent of initial
condition
Implementation complexity is
high
Cuckoo search
Fast convergence, high
efficiency, fewer tuning
parameters
Unknown parameters
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Transactions on Industrial Electronics, vol. 65,
no. 4, pp. 3240-3251, April 2018.

A Review on Maximum Power Point Tracking Techniques under Partial Shading Condition

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A Review on Maximum Power Point Tracking Techniques under Partial Shading Condition