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Maximum Power Point Tracking ofPhotovoltaic System Using Fuzzy Logic

Controller Based Boost ConverterShilpa Sreekumar

Dept. of Electrical & Electronics Engineering,Amal Jyothi College of Engineering,

India.Shilpasreekumar9@gmail.com

Anish BennyDept. of Electrical & Electronics Engineering,

Amal Jyothi College of Engineering,India.

Abstract -Recently, the energy crisis in the world has led tothe rise in use of renewable energy sources. With theadvancement in power electronics technology, the solarphotovoltaic energy has been recognized as an importantrenewable energy resource because it is clean, abundant andpollution free. The efficiency of the photovoltaic system maybe increased by using Maximum Power Point Tracker(MPPT). A number of algorithms are developed to track themaximum power point efficiently. Most of the existing MPPTalgorithms suffer from the drawback of being slow or wrongtracking. Introduction of intelligent MPPTs in PV systems isvery promising. The current paper proposes an intelligentcontrol method for the maximum power point tracking(MPPT) of a photovoltaic system under variable insolationconditions. Here in this paper, intelligent control method usesa Fuzzy Logic Controller applied to a DC-DC converterdevice. The result is compared with the results obtained byusing P&O method. The effectiveness of proposed algorithmis validated by simulation using Matlab/Simulink and iscompared to those obtained by the conventional methods. Theresult shows that the fuzzy logic controller exhibits a betterperformance compared to that of conventional method.

Keywords -Photovoltaic System, Maximum Power PointTracking, DC-DC Converter, P&O Algorithm, Fuzzy LogicController, Artificial Intelligence.

1. IntroductionOne of the major concerns in the power sector is the day

to day increasing power demand but the unavailability ofenough resources to meet the power demand using theconventional energy sources. The continuous use of fossil fuelshas caused the fossil fuel deposit to be reduced and hasdrastically affected the environment depleting the biosphereand cumulatively adding to global warming [1]. Demand hasincreased for renewable sources of energy to be utilized alongwith conventional systems to meet the energy demand. Thusthe growing demand on electricity, the limited stock and risingprices of conventional sources such as coal, petroleum etc hasled to the use of renewable energy sources. Utilization ofrenewable energy resources is the demand of today and thenecessity of tomorrow. With advancement in power electronictechnology, the solar photovoltaic energy has been recognizedas an important renewable energy resource because it is clean,abundant and pollution free. The extraction of maximumavailable power from a photovoltaic module is calledMaximum Power Point Tracking and is done by MaximumPower Point Tracking Controller. The efficiency of the

photovoltaic system may be substantially increased by usingMaximum Power Point Tracker (MPPT) [2]. A number ofalgorithms are developed to track the maximum power pointefficiently. Among all MPPT methods, Perturb and Observe(P&O) method and Incremental Conductance method are mostcommonly used. Most of the existing MPPT algorithms sufferfrom the drawback of being slow tracking. Due to this theutilization efficiency is reduced.

2. Maximum Power Point TrackingA typical solar panel converts only 30 to 40 percent of

the incident solar irradiation into electrical energy. Maximumpower point tracking technique is used to improve theefficiency of the solar panel. According to Maximum PowerTransfer theorem; the power output of a circuit is maximumwhen the Thevenin impedance of the circuit (sourceimpedance) matches with the load impedance. Hence theproblem of tracking the maximum power point reduces to animpedance matching problem. In the source side, a boostconverter is connected to a solar panel in order to enhance theoutput voltage so that it can be used for different applicationslike motor load. By changing the duty cycle of the boostconverter appropriately, the source impedance can be matchedwith that of the load impedance [3].

The efficiency of a solar cell is very low. In order toincrease the efficiency, methods are to be undertaken to matchthe source and load properly. One such method is theMaximum Power Point Tracking (MPPT) [2-3]. This techniqueis used to obtain the maximum possible power from a varyingsource. MPPT techniques are applied on various PVapplications such as space satellite, solar vehicles and solarwater pumping etc. Several methods are presented formaximum power point tracking (MPPT) from photovoltaicsystem such as Perturbation and Observation (P&O) method,Incremental Conductance method, Open Circuit Voltagemethod, Short Circuit Current method, Feedback of powervariation with voltage technique, Feedback of power variationwith current technique, Fuzzy Logic Controller based methodetc [3-6].

In P&O method, the operating voltage is sampled and thealgorithm changes the operating voltage in the required direction[7] [8]. The iteration is continued until the algorithm finallyreaches the MPP [9]. This technique is simple to implement butthe major drawback is the occasional deviation from the maximumoperating point in case of rapidly changing atmospheric conditions[3-4][6]. The Incremental Conductance (Inc-Cond) algorithm isbased on the fact that the slope of the curve power vs. voltage(current) of the PV module is zero at the MPP, positive (negative)

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on the left of it and negative (positive) on the right [10-11]. Thismethod tracks rapidly under varying irradiation conditions moreaccurately than P&O method. It requires complex and costlycontrol circuits [4-6].

Fuzzy Logic based controllers overcome thedisadvantages of conventional methods in tracking maximumpower point. Fuzzy Logic based controller is simple toimplement gives better convergence speed and improves thetracking performance with minimum oscillations [12]. TheFLC based MPPT controls the duty cycle of the dc-dcconverter in standalone system using change in slope of P-Vcurve as input and change in voltage as output [13].

3. The Proposed SystemThe system consists of a PV panel connected to a boost

converter to enhance and regulate the output voltage. It drivesthe DC load by using the power tracked from the solar panel. FThe MPPT controller is used to track the maximum powerfrom solar panel. The block diagram of the proposed system isshown in Fig.1.

Fig.1 Block Diagram Of The Proposed System

A. Photovoltaic SystemPhotovoltaic (PV) are solid-state, semi-conductor type

devices which produce electricity when exposed to light. Theword photovoltaic means "electricity from light." The buildingblock of a solar panel is solar cell. A photovoltaic module isformed by connecting many solar cells in series and parallel.The equivalent circuit of a solar cell is shown in Fig. 2.

Fig.2 Equivalent Circuit Of A Solar Cell

Considering a single solar cell [14], it can be modeledby utilizing a current source, a diode and two resistors. Thismodel is known as single diode model of a solar cell [15-16].The characteristic equation for a photovoltaic cell is given by[15] [17]:

Iph : Light-generated current or photocurrent,Is : Cell saturation of dark current,q : (1.610-19 C ) is the electron charge,

k : (1.3810-23 J/K) is Boltzmann constantT : Cell working temperatureA : Diode ideal factorRsh : Shunt resistanceRs : Series resistance

The characteristic equation of a solar module isdependent on the number of cells in parallel and number ofcells in series [18]. The current variation occurs is lessdependent on the shunt resistance and is more dependent on theseries resistance. Fig.3 shows the P-V, I-V curve of a solarpanel. It can be seen that the cell operates as a constant currentsource at low values of operating voltages and a constantvoltage source at low values of operating current [19].

Fig.3 P-V I-V Curve Of A Solar Cell At Given TemperatureAnd Solar Irradiation

B. Boost ConverterA boost converter (step-up converter) is a dc – dc power

converter with an output voltage greater than its input voltage.Power for the boost converter can come from any suitable DCsources, such as batteries, solar panels, rectifiers and DCgenerators. Since power must be conserved, the output currentis lower than the source current.

Fig.4 Shows the Circuit Diagram of a Boost Converter.

Boost converter steps up the input voltage magnitude toa required output voltage magnitude without the use of atransformer. The main components of a boost converter are aninductor, a diode and a high frequency switch. These in acoordinated manner supply power to the load at a voltagegreater than the input voltage magnitude. The control strategylies in the manipulation of the duty cycle of the switch whichcauses the voltage change [20-21].

When the switch is closed and the inductor is chargedby the source through the switch. The charging current isexponential in nature but for simplicity is assumed to belinearly varying [20]. The diode restricts the flow of currentfrom the source to the load and the demand of the load is metby the discharging of the capacitor. When the switch is openand the diode is forward biased. The inductor now dischargesand together with the source charges the capacitor and meetsthe load demands. The load current variation is very small andin many cases is assumed constant throughout the operation.

C. Maximum Power Point Tracking Controller

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Maximum power point tracking controller used in thispaper is Fuzzy Logic Controller. Fuzzy logic control is aconvenient way to map an input space to output space. Fuzzylogic uses fuzzy set theory, in which a variable is a member ofone or more sets, with a specified degree ofmembership.Recently fuzzy logic controllers have beenintroduced in the tracking of the MPP in PV systems. Theyhave the advantage to being robust and relatively simple todesign as they do not require the knowledge of the exactmodel. They do require in the other hand the completeknowledge of the operation of the PV system by the designer[16]. Fig 5 shows the block diagram of Fuzzy Logic Controller.

Fig.5 Block Diagram Of Fuzzy Logic Controller

A fuzzy logic controller basically includes three blocks.They are Fuzzification, Inference and Defuzzification. Thefuzzy logic controller requires that each input/output variablewhich define the control surface be expressed in fuzzy setnotations using linguistic levels. The process of convertinginput/output variable to linguistic levels is termed asFuzzification. The behaviour of the control surface whichrelates the input and output variables of the system aregoverned by a set of rules. A typical rule would be–“If x is ATHEN y is B” [16]. When all the rules are fired, the resultingcontrol surface is expressed as a fuzzy set to represent theconstraints output. This process is termed as inference.Defuzzification is the process of conversion of fuzzy quantityinto crisp quantity. There are several methods available fordefuzzification. The most commonly used is centroid method.

4. Proposed Fuzzy Logic ControllerFuzzy logic is implemented to obtain the MPP operating

voltage point faster and also it can minimize the voltagefluctuation afterMPP has been recognized. The proposed fuzzylogic based MPPT controller has two inputs and one output.The error E(k) and change in error CE(k) are the inputvariables to Fuzzy Logic Controller and is given below inequation 2 & 3 for kth sample time [22] [23]

E (k) = dP/dV = [PPV (k)-PPV (k-1)]/ [VPV (k)-VPV (k-1)] (2)

CE (k) = E(k) - E(k-1) (3)

Where Ppv(k) denotes the power of photovoltaic panel.The input variable E (k) represents the error which is definedas the change in power with respect to the change in voltage.Another input variable CE (k) expresses the change in error.The output of the Fuzzy Logic Controller is duty cycle (D)which should be given to the boost converter.

Fig.6 represents the Fuzzy Logic Controller in which E(k) and CE (k) are the input variables and D as the outputvariable.

Fig.6 Fuzzy Logic Controller

To design the FLC, variables which can represent thedynamic performance of the system to be controlled, should bechosen as the inputs to the controller. The input and outputvariables are converted into linguistic variables. In this case,five fuzzy subsets, NB (Negative Big), NS (Negative Small),ZE (Zero), PS (Positive Small) and PB (Positive Big) havebeen chosen.

Fig.7(a) Membership Function for e(k)

Fig.7(b) Membership function for CE(k)

Fig.7(c) Membership function for D

Membership functions used for the input variables andoutput variables are shown in Fig.7(a), Fig.7(b) andFig.7(c)respectively. A fuzzy rule base is formulated for the presentapplication and is given in table 1. The fuzzy inference of theFLC is based on the Mamdani’s method which is associatedwith the max-min composition. The defuzzification techniqueis based on the centroid method which is used to compute thecrisp output.

Table 1. Fuzzy Rule Table

ECE NB NS ZE PS PBNB ZE ZE PB PB PB

NS ZE ZE PS PS PSZE PS ZE ZE ZE NSPS NS NS NS ZE ZEPB NS NB NB ZE ZE

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5. Simulation And ResultsThe PV module is modelled in MATLAB using

equation 1 with the assumption that the PV module hasconstant temperature of 250C. PV model is shown in the Fig.8.

Fig.8 PV Model

Fig.9 P-V Characteristics Of A PV Module

Fig.10 I-V Characteristics Of A PV Module

P-V and I-V characteristics of PV module at aninsulation level of 1000W/m2 and 25oC temperature is shownin Fig. 9 and Fig. 10 respectively. Electrical characteristics ofthe modeled PV are given in the Table 2.

Table 2. Electrical Characteristics of PV Cell

Maximum power 150W

Voltage at Pmax( Vmax) 34.5V

Current at Pmax 4.35A

Warranted minimum Pmax 140W

Short circuit current 4.75A

Open circuit voltage 43.5

The maximum power point tracking based on FuzzyLogic Controller and P&O algorithm were tested under varyingsolar irradiance and at a constant temperature of 250C. Thesimulink model includes a PV model, a pure resistive loadwhich is connected to the PV module through a dc-dc boostconverter to enhance the output voltage from PV and MPPTcontroller to track maximum power. The sampling time takenfor this simulation is 5µs.

Fig.11(a) Simulink Model using Fuzzy Logic Controller

Fig.11(b) Simulink Model using P&O Algorithm

The inputs of the PV module are the solar irradianceand the ambient temperature. The performance of the proposedtechnique has been examined for variable solar radiance and ata constant temperature of 250C. The output produced by thePV module is the PV current, which acts as a controlled currentsource for the input of the boost converter. The load resistance,R taken is 150 Ω. The MPPT block consists of MPPTalgorithm, namely Fuzzy Logic Controller and P&O algorithm.The output of the MPPT controller is given to discrete PWMgenerator for pulse width modulation. The switchingfrequency, fs was set to be 25 kHz in this simulation. Fig.11 (a)and Fig. 11(b) show the configurations of MPPT algorithms inSimulink using Fuzzy Logic Controller and P&O algorithm.

Fig.12 and Fig.13 shows the power response obtainedusing Fuzzy logic controller based MPPT and Perturb &Observe algorithm. From the above results it seems that the PVpower which is controlled by the proposed Fuzzy LogicController is more stable than the conventional MPPTtechniques. The power curve obtained with FLC is smootherwhen compared to P&O algorithm. Fig.14 and Fig.15 showsthe output voltage of boost converter using Fuzzy LogicController.

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Fig.12 Power Response Obtained Using Fuzzy LogicController

Fig.13 Power Response Obtained Using P&O Method

Fig.14 Voltage Response Obtained Using FLC Method

Fig.15 Voltage Response Obtained Using P&O Method

6. ConclusionTwo MPPT control algorithms, namely Fuzzy Logic

Controller and P&O were discussed and reviewed. The studypresents a simulation comparison of the maximum powerproduced for Fuzzy Logic Controller based method and P&Oalgorithm. The P&O algorithm is the simplest method, whichresults in low cost of installation and it may be competitivewith other MPPT algorithms. On the other hand, the FLCmethod provides a simpler way to arrive at a definite

conclusion, but it highly depends on the user’s knowledge ofthe process operation for the parameter setting of Fuzzy LogicController. The conventional MPPT algorithms are not capableof tracking maximum power rapidly under varyingatmospheric conditions.

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