IRJET- Classification and Comparison of Maximum Power Point Tracking Algorithms for Photovoltaic System: A Review

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 4897
Classification and Comparison of Maximum Power Point Tracking
Algorithms for Photovoltaic System: A Review
Radhika Wasekar1, Nilesh Chamat2
1PG Scholar, Dept. of Electrical Engineering, Ballarpur Institute of Technology, Balharshah, Maharashtra, India
2Assistant Professor, Dept. of Electrical Engineering, Ballarpur Institute of Technology, Balharshah,
Maharashtra, India
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Abstract - Photovoltaic (PV) electrical power generation,
as one of renewable energy source, is a technique which uses
solar energy to produce electric energy. The utilization of
such systems is growing because of limited resources and
the acute energy crisis. This motivates the researcher
community to reaches these renewable energy sources also
to maximize their efficiency. This paper presents a literature
survey from most up-to-date achievements in maximum
power point tracking system AI algorithms. Maximum
power tracking algorithms are used to match the load
resistance to the supply input resistance to extend the power
delivered from the photovoltaic system. This paper
compares between the works done in these algorithms
concentrating on the AI algorithms which have proven
higher efficiency in this field. The paper additionally states
the foremost recent contributions in every algorithm.
Key Words: Photo-voltaic (PV), Maximum power point
tracking (MPPT), Traditional Control Techniques,
Intelligent Control Techniques, Hybrid Intelligent
Control Algorithms.
1. INTRODUCTION
Recently, energy generated from clean, renewable,
efficient, and environmental friendly sources has become
one of the of the major research areas for scientists and
engineers. solar power systems attract more research,
among all renewable energy sources, because of their
availability. Wide usage of photovoltaic systems led to the
reduced cost of manufacturing, but still the problem of low
efficiency of the solar panels. The output powers of PV
system are crucially depending of the two variable factors,
which are the cell temperatures and solar irradiances. This
make the solar panel efficiency can reach 30-40%. This
means that up to 40% of the incident energy is converted
to electricity. The techniques to utilize effectively the PV
are known as a maximum power point tracking (MPPT)
method. These techniques are used to extract the
maximum accessible power from PV module by creating
them operates at the foremost efficient output.
In order to obtain the MPP we need a technique to force
the controller to operate at the optimum operating point.
Many tracking control techniques have been developed
and implemented. The common techniques that has been
used varies from traditional techniques such as Hill
Climbing/Perturb and Observe, constant voltage to
computational intelligence techniques such as neural
network and fuzzy logic [1-2]. Actually, the intelligent
control fields [3-5] have versatile control methods or
algorithms like artificial neural networks, fuzzy logic,
particle swarm optimization, artificial bee optimization,
cuckoo search and evolutionary algorithms for a variety of
tasks in control. These techniques are alternatives to get
satisfying controllers by training employing a data set. At
the same time, these techniques have some drawbacks
such as failing to perform under partially shaded
irradiance conditions, and their cost and complexity are
high.
In this paper, a broad survey for the computational
intelligence techniques and their application in tracking
the MPPT in photo-voltaic system is presented. The entire
paper is organized as follows. Section 2 briefly introduces
the concept of MPPT. Section 3 is introduces the different
traditional/conventional control techniques for MPPT.
Section 4, presents the intelligent control techniques for
MPPT. Section 5, introduces new hybrid AI techniques.
Finally Section 6 presents the conclusions.
2. CONCEPT OF MAXIMUM POWER POINT
The maximum power point principle is based on the
circuit principle: when the photovoltaic cell's output
impedance and the load impedance are equal. The output
power of photovoltaic cells is maximum. The control
algorithm tracks the maximum power point which can be
affected by climate conditions such as: temperature and
irradiance. As shown in Fig. 1, the relationship between
voltage and current is non-linear. Along the IV curve, there
exists a point where the solar panel will output its
maximum power; this is called the maximum power point.
This principle seems easy to carry; however, there are
several limitations due to local maximums and oscillations
around the maximum point during the search for this
point.

Fig -1: The relation between the characteristic I(v) of a cell
and a load resistor.
Due to such limitations which can be summarized that the
voltage power characteristic of a photovoltaic (PV) array
is nonlinear and time varying because of the changes
caused by the atmospheric and load conditions. The MPPT
principles is to control the duty cycle for the pulse width
modulation block that controls the power converter to
deliver maximum power to the load as shown in Fig. 2.
Fig -2: Block diagram of a MPPT controlled PV system.
3. MPPT TRADITIONAL CONTROL TECHNIQUES
3.1 Incremental conductance (INC) MPPT
algorithm
INC is commonly used for solar PV MPPT. The incremental
conductance method is based on the fact that the slope of
the P vs. V (I) of the PV module is zero at the MPP, positive
(negative) on the left of it and negative (positive) on the
right of MPP. This technique deals with the sign of dP/dV
without a perturbation which overcome the limitations of
P&O technique [5].
dP/dV > 0 left side of the curve
dP/dV < 0 right side of the curve
dP/dV =0 peak of the curve
The above expressions can be expressed as (shown in fig.
4):
(1)
For MPP by putting , we get,
Hence,
∆I/∆V= -I/V , At MPP
∆I/∆V > - I/V, Left of MPP
∆I/∆V < - I/V, Right of MPP
Where,
I/V is instantaneous conductance,
∆I/∆V is incremental conductance,
VREF is reference voltage at which PV array is to be
operated.
According to above equations the maximum power point
of PV array can be tracked by comparing the I/V to ∆I/∆V
as shown in the flow chart (fig. 7).
Fig -3: Flow chart of Incremental Conductance method.
When the MPP is achieved at that instant VREF must be
equal to VMPP. And once it happens the operation is
maintained at MPP until a change in ∆I is occur or the
change in atmospheric conditions. The INC algorithm is
continuously decreases or increases the VREF to maintain
the new MPP. This method has advantages over P&O
method like INC technique can track rapid change in
atmospheric conditions. Also this technique determines
when it has reached the MPP whereas the P&O technique
oscillates around the same point [1], [2], [4]-[6], [8].

Fig -4: I-V and P-V curve and maximum power point of PV
module.
3.2 Hill climb search (HCS) MPPT algorithm
The Hill climb search (HCS) MPPT algorithm is also called
perturbation and observation (P&O) MPPT algorithm. In
Perturb-and-observe algorithm method, we only use one
sensor and hence it is very easy to implement. Voltage
sensor used, senses the PV array voltage and so the cost of
implementation is less among all other MPPT algorithm.
The Perturb-and-observe algorithm for maximum power
point tracking is simplest techniques among all the MPPT
techniques in literatures. It is based on the simple
mathematical condition, i.e. dP/dV = 0, where P and V are
power and voltage at output of PV module respectively.
From fig. 1, it can be seen that increase in voltage
increases power when the PV array operates in the left of
MPP and power decreases on increasing voltage when the
same is operates in the right of MPP. Hence if dP/dV > 0,
the perturbation should be same and if dP/dV < 0, the
perturbation should be reversed. The process should be
repeated periodically until dP/dV = 0 reached (maximum
power point) [1], [3], [4], [7].
Fig -5: Flowchart of P&O method.
Under sudden changing atmospheric conditions P&O
method does not respond well as illustrated in figure 6.
Due to small perturbation of ΔV in the PV voltage V under
constant atmospheric conditions the operating point
moves from A to B. Since power decreases to B so
according to P&O algorithm the perturbation should be
reversed. And when the power curve shifts from P1 to P2
due to increase in irradiance the operating point will
change from A to C. Now there is increase in power so
again according to P&O algorithm the perturbation
should be kept same which results in the divergence of
operating point from Maximum Power Point [3], [4] and
hence calculates the wrong MPP. To avoid this problem we
can use incremental conductance method to track MPP
correctly even under rapid change in irradiance.
Fig -6: Divergence of P&O from MPP.
4. MPPT INTELLIGENT CONTROL TECHNIQUES
4.1 Fuzzy Logic
Fuzzy logic was 1st introduced by a great mathematician
Loftih A. Zadeh of university of California at Berkeley. The
theory wasn’t popular at first and its applications weren’t
clear. Fuzzy logic control uses human expert knowledge to
make control decisions. Fuzzy logic are often used in the
treatment of unknown systems to model inexact data and
experience |and skill} knowledge. The fuzzy controller
block diagram is shown in Fig. 7. The fuzzification block is
responsible for converting the numerical input variables
to linguistic variables in accordance with the membership
functions. The Fuzzy inference is that the process of
formulating the mapping from a given input to associate
output using fuzzy logic. The defuzzification block
converts the linguistic output from the inference engine to
numerical output values using the membership function.
Fuzzy rule base refer to a set of predefined instructions
which link the different values of crisp values with
different subsets of fuzzy output space.

Fig -7: Fuzzy Controller Architecture.
The inputs to the fuzzy control are the error in the power
and the change of error the output is the duty cycle
variable that controls the pulse width generation block.
The error is given by the following equation.
(2)
The change in error is given by
(3)
The output of the fuzzy controller is that the duty cycle
(4)
A comparison between P&O and fuzzy controller for
maximum power transfer under different weather
conditions is introduced [9-10].A simulink model and a
hardware implementation is presented [11-12]. A
simulation and software implementation of fuzzy logic
controller and a hardware implementation are presented
[13].
4.2 Artificial Neural Network
Artificial neural networks are one of machine learning
techniques which have been developed as generalizations
of mathematical models of biological nervous systems. The
learning capability of a synthetic neuron is achieved by
adjusting the weights in accordance to the chosen learning
algorithmic rule. The learning situations in neural
networks may be classified into three distinct types,
supervised learning, unsupervised learning and
reinforcement learning. The most widely-used neural
network for prediction is the single hidden layer feed-
forward network.
There are two ways in literature for applied neural
network controller in photovoltaic:
1- Using the neural network as a controller to regulate the
duty cycle of the pulse width generator block. This
allows the output resistance to match the load
resistance.
2- The second method is using the neural network as a
reference for the maximum voltage and current points
Vm, Im, and using another controller such as fuzzy
controller to track the maximum power point.
In this section, the previous work that uses the first
method is presented, while the second method will be
presented in the next section. In [14], a comparison
between a neural network controller and P&O is
presented and the simulation results show that ANN has
fast and precise response under fast changes of solar
irradiation. A PC based neural network controller for
maximum power point tracking is presented [15]. A back
probagation trained neural network MPPT controller is
introduced [16]. A fast tracking algorithm under fast
environment variations is presented [17]. In [17],
differential evolution technique is used to train the neural
network.
5. HYBRID INTELLIGENT CONTROL ALGORITHMS
5.1 ANFIS
The adaptive-neuro fuzzy inference system is a hybrid
system that combines the potential benefits of both the
artificial neural network and fuzzy logic. This technique
has been employed in many modeling and forecasting
problems.
A comparative study between neuro-fuzzy controller and
P&O algorithm is presented [18]. The study proves the
efficiency of the neuro-fuzzy controller. A simulation
based comparative study between neuro-fuzzy and fuzzy
controllers is introduced [19]. An ANFIS controller with
cuk converter is presented [20]. An advanced neuro-fuzzy
controller is introduced [21]. A comparative study
between five different maximum power point tracking
techniques including neuro-fuzzy is presented [22].
5.2 Intelligent P&O
Integrating the P&O algorithm with intelligent techniques
will assist to enhance its performance and get better
results. In [23] the authors present that the neural
network enhanced P&O. In this work the neural network is
used to decide the variable step for the P&O algorithm this
enhances the algorithms stability and decreases the
oscillations around the MPP. Decreasing the oscillations
around the MPP reduces the power loss which is an
important feature for this algorithm. The same idea can be
implemented using fuzzy logic instead of a neural network
[24]. In this paper a fuzzy logic block is introduced to
control the step size of the P&O.
The second method is to replace the decision making
blocks in the flow chart with the fuzzy logic controller. In
this case the fuzzy controller produces the duty cycle for
the pulse width generation [25-27].

5.3 Hybrid Genetic Algorithm
Genetic algorithm is the most important evolutionary
algorithms. The genetic algorithm is an effective research
algorithm that can search a large complex solution space
for an optimum or near optimum solution. To optimize
fuzzy controllers or to optimize neural network to control
the MPP GA algorithms are used. The main idea of the
genetic algorithms is to mimic the evolution theory. The
algorithm reaches an optimal set of parameters using the
"survival of the fittest" principle. A neural network genetic
algorithm optimized controller is presented [22]. A fuzzy
logic genetic algorithm optimized controller is introduced
[23-24]. Other work introduces using the genetic
algorithm as a controller for the maximum power point
tracking and a comparative study is done [24-26].
5.4 Fuzzy-PID
The PID controller is a conventional controller which is
used in most of the other control applications. The PID is
stands for proportional integral differential controller. The
output of the PID controller depends on three constant
one for the proportional term and one for the integral
term and the last one for the differential term. There are
many methods for tuning the PID controller that is to find
the proportional, integral and differential gains. The most
widely used method in tuning the PID controller is the
Ziegler-Nichols tuning formula. There are two approaches
for using fuzzy logic and PID block in control the first
approach is to use the fuzzy logic block as a tuning block
for the PID controller. A new adaptive fuzzy PID controller
for maximum power point tracking is introduced. The
fuzzy block is used for tuning the PID controller online
[27]. The work also introduces a comparison between the
fuzzy tuned PID controller and the conventional PID
controller and the P&O controller that proves the high
tracking capabilities of the algorithm. The same idea was
implemented in other work [28] the block diagram for this
approach is given in Fig. 8.
Fig -8: Fuzzy PID Controller Architecture.
The second approach is to use the fuzzy controller to
introduce or to get some other control signal for the PID or
the PI to work on an example of this approach is given [29-
31]. The block diagram is shown in Fig. 9.
Fig -9: Fuzzy PI Controller Architecture
5.5 Ant Colony Optimization
The Ant colony optimization (ACO) is a probabilistic
research algorithm for the optimum path. The Ant colony
is used in the MPPT in two approaches: first as a direct
controller to find optimum power point instead of finding
the optimum path. The second approach as an optimizing
tool for PI or Fuzzy controller. A novel Ant colony
maximum power point tracking controller for PV systems
under shading conditions was introduced [34]. A PI
optimized controller for maximum power point tracking
was also presented in [32]. A fuzzy controller optimized
with Ant colony algorithm is presented [33].
5.6 Fuzzy-Neural Network
Instead of using the ANFIS controllers, there is another
form of hybridization that combination of neural network
and fuzzy algorithms. These type of hybrid techniques are
always mentioned with two approaches in the literature.
The first approach is to use the neural network to estimate
some variable for the fuzzy logic controller [34-35]. The
second approach is to use the fuzzy logic with Hopfield
neural network to control the maximum power point [36-
37].
5.7 Other AI techniques
In this section we will discuss other AI techniques which
are not frequently referenced in the literature. A new
neural network improved algorithm is introduced [38-39]
the new algorithm uses a neural network to enhance the
performance of the increment conductance algorithm. The
neural network computes a reference voltage value for the
algorithm to work on. The algorithm is tested on different
irradiation and partial shading conditions. A fuzzy
differential evolution controller is introduced.
6. COMPARISON OF MPPT TECHNIQUES
This section offers an outline of the most characteristics of
the MPPT controller techniques presented in an
exceedingly comparative means. However, the analysis of
control techniques is completed along a set of analysis

criteria. These include complexity, learnable, response
time, and power consumption. The results are
summarized in Table 1.
Table -1: Comparison of MPPT techniques with respect to
several parameters
Techniques
Parameters
Complexity Learnable Response
Time
Power
Consumption
INCD Simple No Slow Loss
P&O Simple No Slow Loss
Fuzzy Complex No Fast Efficient
ANN Complex Yes Fast Efficient
ANFIS Complex Yes Slow Efficient
I P&O Complex No Medium Loss
Fuzzy
PID
Complex No Fast Efficient
GA Complex Yes Fast Efficient
AC-Fuzzy Very complex No Fast Efficient
Fuzzy-Neural Very complex Yes Fast Efficient
7. CONCLUSIONS
From many years researchers and scientists are working
on renewable energy sources. MPPT is the technique for
increasing the output efficiency and mainly used for solar
system and play vital role in electrical energy generation.
In this study, general classification and descriptions of the
most widely used seven MPPT techniques are analyzed
and compared to point out the advantages and drawbacks
of various MPPT methods. This paper is helpful for
selecting a MPPT technique depending upon various
constraints as given in the table.
Intelligent controller techniques have higher performance
in tracking the maximum power point. Moreover, they are
efficient, adaptive and robust search methods producing
near optimal solutions and have a large amount of implicit
parallelism. However, the main drawback that plagues the
intelligent techniques-based MPPT algorithms is its
complexity, the large number of control parameters and
high computations. Which are not suitable for low power
applications.
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