A FUZZY-BASED COMBINED MPPT TECHNIQUE...

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International Journal of Electrical Engineering & Technology (IJEET)

Volume 9, Issue 4, July-August 2018, pp. 33–45, Article ID: IJEET_09_04_004

Available online at http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=9&IType=4

ISSN Print: 0976-6545 and ISSN Online: 0976-6553

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© IAEME Publication

A FUZZY-BASED COMBINED MPPT

TECHNIQUE FOR WIND AND SOLAR SYSTEM

Dr Sudhir Sharma

Associate Professor and Head of the Department,

Electrical Engineering Department, DAV Institute of Engg. & Tech., Jalandhar, India

Sandeep Kaur

Student, Department of Electrical Engineering,

DAV Institute of Engg. & Tech., Jalandhar, India

ABSTRACT

Maximum Power Point Tracking has been using to enhance the capability of the

power generation systems. There are two different power generation systems such as

solar and wind which have been used by several researchers. Alternatively, in this

paper, hybridization of these systems is done which are controlled by the fuzzy logic. It

has combined with the system to make a decision about the participation of MPPT in

the model. The simulation analysis has performed using the MATLAB Simulink model

where wind and solar both systems are operated. From the results analysis, it has

shown that the proposed system concludes fewer fluctuations as well as less distortion

in the system.

Key words: Maximum Power Point Tracking, Solar energy, Wind energy, Total

Harmonic Distortion.

Cite this Article: Dr Sudhir Sharma and Sandeep Kaur, A Fuzzy-Based Combined

MPPT Technique for Wind and Solar System. International Journal of Electrical

Engineering & Technology, 9(4), 2018, pp. 33–45.

http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=9&IType=4

1. INTRODUCTION1

There is a variable source of energy that is available on earth such as coal, petroleum, natural

gasses etc. These resources can be categorized into two types as follows: Renewable Sources of Energy

Non-Renewable Sources of Energy

Renewable Sources of Energy: The renewable sources of energy generation are solar, wind,

rain, tides, geothermal heat etc [1]. These sources of energies generally opt the energy from

natural resources.

A Fuzzy-Based Combined MPPT Technique for Wind and Solar System


Nonrenewable Source of Energy: The nonrenewable resources are such as coal, oil,

petroleum, etc. which can be exhausted totally if used in excess. There is a need to conserve

nonrenewable sources of energy, therefore, these are also known as finite sources of energy

[2]. The example of a non-renewable source of energy is minerals, ores that are extracted

from the crust of earth by the time when the natural geographical process takes place which

makes sources viable to extract for economical use.

As the human being is overusing the energy sources such as petroleum, coal, a natural oil

which will results in depletion of these resources within coming hundred years.

Hence there is a need to shift the focus on the renewable source of energy in spite of

nonrenewable sources of energy because; there is no risk of depletion of the renewable source

of energy. In 2012, it was surveyed that 19% of energy was consumed in that year and this

number observed to be increased in 2013 and 2014 as per renewable global status survey

[3][4].

Given table depicts the summarized report generated by a survey that point to global rapid

enhancement in renewable energy on the basis of last few years from 2010-2013.

Table 1 Necessary global indicators for renewable energy

S. No. Energy Sources (Gw) 2010 2011 2012 2013

1. Renewable power

installed capacity with

hydro

1,250 1355 1470 1560

2. Renewable power

installed capacity

without hydro

315 395 480 560

3. Solar PV installed

capacity

40 71 100 139

4. Wind power installed

capacity

198 238 283 318

5. Concentrating Solar

Thermal Power installed

capacity

1.1 1.6 2.5 3.4

From the above-defined tables and points, it is observed that the natural resources are

depleting day by day for that reason in order to fulfil the required need of energy the power

sector is researching for substitute of these resources.

By using the renewable energy, the carbon molecules that are available in the atmosphere

can be decreased through which the issue of global warming can be overcome [5]. There are

various renewable sources are available out of which solar PV systems or wind PV systems

are most prominent sources due to its simple and easy implementable topology.

PV system is a device which is made up of semiconductors which are in the solid state.

These semiconductors are used to generate electric power when it comes in the touch with

light [6]. A PV device can be created by interconnecting these solar cells either parallel or in a

series. It is better to connect these solar cells in series to achieve the high output voltage. The

features like long-lasting benefits and free of maintenance cost make PV systems more useful.

Dr Sudhir Sharma and Sandeep Kaur


Figure 1 PV generation System

PV devices are mainly divided into categories as per their functionalities and operations

performed by the devices, modules, the configuration of the system [7]. Grid-connected and

standalone systems are two main categories of PV systems. These systems are created to

produce DC and AC power energy to operate grid on an individual basis which is connected

with other storage devices and energy sources.

2. MPPT (MAXIMUM POWER POINT TRACKING)

MPPT stands for maximum power point tracking. It is a control technique or method which is

basically employed with energy resources in order to extract the maximum capable energy of

PV modules [8][9]. The objective behind employing MPPT is to enhance the efficiency of PV

system. A large number of algorithms is created for MPPT control.

2.1. Types of MPPT Algorithms

Various MPPT techniques have been developed form last few years. These techniques are

divided as follows:

1. Online methods

a. VMPPT

b. CMPPT

c. Look up Table

2. Offline methods

a. P & O

b. Incremental

c. Temperature

d. 3 Point MPPT

3. Indirect methods

a. ANN

b. Load Parameter

c. RCC

Out of above-defined techniques, offline methods are solar cell dependable methods.

Figure 2 depicts the categorization of MPPT techniques. In this the three main categorize are

further subdivided into modules as online MPPT techniques have further 4 more techniques

under its category, similarly, the offline technique consist 5 subcategories and indirect



technique is divided into three more types. Most of the MPPT techniques suffer from the

problem of slow tracking which directly degrades the utilization efficiency of the system [10].

2.2. Need for MPPT

The need for MPPT arises due to disadvantages of PV systems. By deploying PV system, if

the system tries to extract more energy as compared to the amount of energy that is produced

by the system then there is a requirement of extra cost which is not feasible to implement. The

MPPT is an electrical DC to DC converter or device that optimizes the similarities among PV

devices, in this way it enhances the efficiency of power system [11]. To enhance the

capability of power generation PV devices modifies the high voltage output that is received

from the renewable source of energy to lower voltage power which is required to run the

batteries. In MPPT DSPs are used to handle the converters [12]. The working of MPPT starts

by receiving the input from PV systems then converts this power to high voltage DC voltage

power and then splits it to the various DC voltage currents

2.3. Fuzzy Logic Control based MPPT techniques.

From last few years, fuzzy logic based MPPT mechanism is most popular among the research

workers in this field. The fuzzy logic based MPPT technique operates on the basis of

estimated inputs and it is also able to manage the non-linear functions and did not require an

exact model or match for processing [13]. Above defined characteristics of the fuzzy logic

system makes it more popular among the researchers in various fields. The working of the

fuzzy logic system is divided into three various states such as Fuzzification, decision making

and Defuzzification.

Following figure 3 explains the working process of the fuzzy system in brief. Firstly a

crisp value is added to the fuzzy system as an input. Then Fuzzification process is applied to

the crisp fuzzy values. Fuzzification is a process which converts the crisp values into fuzzy

sets [20].

Figure 2 Fuzzification [34]

Then defined rules are applied to the fuzzy input set driven by applying fuzzification. On

the basis of rules an intelligent decision is taken and then the fuzzy sets are converted to the

crisp values back by applying the Defuzzification [14].



Fuzzy Input Fuzzy Output

Figure 3 Working of Fuzzy Logic Based System

Fuzzification: It is a process in which the numerical values are converted to linguistic

variables. The input membership function is defined on the basis of any interval such as [-1

1]. In case of the power system, PV array and MPPT voltages are two inputs that are used to

define the control values.

Decision making phase: In this phase, the system generates output by using input functions

on the basis of If-Then rules set. Fuzzy logic contains a fuzzy rule set which defines the rules

in the form of If-Then.

Defuzzification: It is a process in which to manage the power and to control the operating

point to MPP analogue signals are generated.

3. PROBLEM FORMULATION

Energy is important for the human life and economy. Consequently, due to the increase in the

industrial revolution, the world energy demand has also increased. In the later years, irritation

about the energy crisis has been increased. Fossil fuels have started to be gradually depleted.

It is a global challenge to generate a secure, available, and reliable energy and at the same

time reduce the greenhouse gas emission. Energy saving was suggested by the researchers to

meet the worldwide energy demand. But this method is a cost-effective solution. One of the

most effective and suitable solutions is the renewable energy supplies. Recently an MPPT

strategy was proposed to extract maximum power from a variable speed wind turbine based

on squirrel cage induction machine drive by using a fuzzy logic control. The main issue that

was analyzed in the previous model was that the till date most of the researchers work on the

Pv array based MPPT systems only a few did work on wind-based power source but no one

worked on combining both sources. Also if it will be done there will need to decide when it is

to be switched so further work can be done in the proposed model.

4. PROPOSED WORK

As per the study, it was analyzed that no work was done on combining the multi-source

models as PV that is solar and the wind source. So in the proposed model, it is proposed that

the model will have both the solar and the wind as a source and a hybridized model. Also, a

new thing will be used that the switching will not be done manually that will be done using a

fuzzy model that which source will be switched on. Finally, the rest MPPT will work on the

fuzzy control based simulation. The whole working will be done using the MATLAB

Simulink.

Crisp

Input Crisp

Outp

ut

Rules

Defuzzification Fuzzification

Intelligence



5. MATHEMATICAL MODELLING AND SIMULINK MODEL

Mathematical Model of PV Array

In the proposed work, both source models have been utilized. The general mathematical

model of PV array has been studied over past years. This equivalent circuit consists of

different models such as a photocurrent, a diode, a parallel resistor expressing a leakage

current and a series of the resistor. These components defined the internal resistance to the

current flow. The figure depicts this idea below:

Figure 4 Circuit diagram of the PV model

The equation of solar cell is given below. This equation is of voltage-current

characteristics such as:

( ( )

)

(1)

In the above equation 1, Iph is considered as the light generated current or photocurrent.

Io is the cell saturation of dark current.

The electron charge is considered as q = 1.6 * 10-19

C and K = 1.38* 10-23

J/K is the

Boltzmann’s constant. Other parameters such as T, N and Rsh are considered as the cell’s

working temperature, ideal factor and shunt resistance respectively. And Rs is a series

resistance in the equation 1. The photocurrent is totally dependent upon the solar irradiance as

well as on the cell temperature which can be derived in the below equation as:

( ( )) (2)

In the equation 2, Iscr represents the cell’s short current at a 250 C and 1 kW/m

2. Ki

symbolizes the cell’s short circuit current temperature coefficient. Tr is the cell’s reference

temperature and G is the solar irradiance in Km/m2.

With the variation in the temperature of the cell, the saturation current of the cell also

varies. The equation to describe this variation is discussed below:

(

) (

(

)

) (3)

Tc= ((NOCT-20) *

) + (Ta) (4)

In the equation 3, IOR signifies the cell reverse saturation current at a reference

temperature as well as a solar irradiance. The Nominal operating cell temperature symbolizes

through the NOCT. The band gap energy of the semiconductor in the cell shows as eg. The



variable N is considered as the ideality factor which is dependent on the PV technologies.

Thus, the concluded behaviour of the PV cells are described using five parameters such as

Iph, N, Is, Rsh. This model is used to represent the physical PV cell/module. The five

parameters mentioned are related to the two different environmental parameters such as solar

irradiance and the temperature.

Mathematical Model of Wind

The method which is used to extract the amount of power from the wind is Wind Turbine.

And the expression which is used for such purpose is given as:

.. (5)

(6)

With λ

(7)

Cp in the equation 5 shows the ability of a wind turbine in order to extract the power from

the wind. This parameter is a complex function of λ and . The Cp parameter is considered as

the power coefficient. This coefficient parameter is used to represents the fraction of a power

in the wind which is captured by the wind turbine. The power coefficient can be represented

in the following formula as:

( ) (8)

Where the equation 2 shows the pitch angle of the blade in degrees with the symbol β and

tip speed ratio of the turbine is shown with γ.

Mathematical Model of MPPT

The mathematical model of the MPPT which has used in designing the proposed model has

described in this section. The MPPT can be defined in terms of two different conditions

shown as:

( )

(9)

( )

( )

( ) (10)

In the above equation (10), (a) represents the condition at MPP, and (b) and (c) represents the

condition on left and right respectively.

Moreover, the beta method is used to approximate the point of maximum power using the

equation of variable which is independent i.e. (11)

(

) (11)

In the equation 11, c represents the (q/ (σ.K.T.Ns)) which is constant depending on the

electric charge q,



σ= quality factor of the junction panel,

K=Boltzmnn constant,

T= Temperature and

Ns= amount of PV cells in series

As the conditions of the operation can be change but the value of β remains constant this

can be used to calculate the voltage as well as the current of the panel and further, be inserted

into a conventional closed loop having a constant reference.

Figure 5 Proposed model of MPPT

The above method described the proposed model of MPPT where fuzzy has combined to

it to generate an optimum solution and perform switching between two different power

systems i.e. wind and solar.

6. RESULTS

This section of the paper explained the results acquired after performing the proposed method.

In the proposed method solar and wind have combined together. Moreover, it uses the fuzzy

logic decision model to decide when the MPPT will be participating. The experiment analysis

was performed using the MATLAB Simulink model to examine the results of the proposed

method.

Figure 6 Fuzzy model of the proposed work

In this model wind and solar units are combined and an MPPT controller is designed for

the system to extract maximum power from the resources. The MPPT is controlled by the

fuzzy logic.



Figure 6 represents the fuzzy model of the proposed work. This fuzzy model is used to

switch between two different power generation systems wind and solar. For the proposed

model, two different inputs such as error and change error and one output i.e. Voltage

reference has taken. Based on the given input, an output has produced.

Figure 7 Membership function of the first input parameter

Figure 7 shows the membership function of the first input parameter such as Error. The

above membership function ranges from 0 to 1. Likewise, figure 8 and 9 also depicts the

membership function of another input i.e. change error and one output function i.e. voltage

reference respectively.

Figure 8 Membership function of the second input parameter

Figure 9 Membership function of the output parameter

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.2

0.4

0.6

0.8

1

Error

Degr

ee o

f mem

bers

hip

NB NS ZE PS PB

-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0

0.2

0.4

0.6

0.8

1

ChangeError

Degr

ee o

f mem

bers

hip

NB NS ZE PS PB

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

0

0.2

0.4

0.6

0.8

1

VoltageReference

Degr

ee o

f mem

bers

hip

NB NS ZE PS PB



Figure 10 Voltage of the proposed model

The figure 10 shows the voltage of the proposed model. From the result it has been

cleared that the number of distortions in the voltage are less and the system is more stable.

Figure 11 Total Harmonic Distortion

Figure 11 shows the THD i.e. total harmonic distortion of the proposed model. It

calculates the power quality of the electronic power system. The lesser the distortion, more

accurate the reproduction of the signal will be. The figure concludes that THD is quite less in

the proposed model.

Figure 12 The current of the proposed model

The figure 12 represents the current of the proposed model. It depicts the three-phase

current which varies from the -6 to 6.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

100

200

300

400

500

600

700

800

Time

Volta

ge

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20

50

100

150

200

250

300

Time

THD

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-15

-10

-5

0

5

10

15

Time

Curre

nt



Figure 13 Analysis of the proposed system

The figure 13 characterizes the analysis of the proposed system in terms of current, Total

Harmonic Distortion and the voltage. The numbers of fluctuations in the proposed model are

less which confirms that the system is a more stable

7. CONCLUSION AND FUTURE SCOPE

From the experimental analysis using the Simulink model has shown that the proposed model

is efficient. It has been explored over three different system analysis parameters such as

Current, Voltage and the THD. The three-phase current of the proposed model varies from -6

to 6. And the voltage acquired from the system has less number of fluctuations which

concludes more stability. Lastly, the less distortion of the system confirms high accuracy.

Consequently, the proposed model using the fuzzy logic with hybridization of two different

power generation systems ensures more stability and accuracy.

The proposed model combines the energy power of both the system for the utilization.

Thus, in case if the energy of both the system goes down, the demanding energy will not be

fulfilled. Therefore, in future, diesel generator can be introduced to accomplish the desired

requirement of the energy.

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A FUZZY-BASED COMBINED MPPT TECHNIQUE...

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