Fuzzy Logic Controller Based Maximum Power Point

Tracking of Photovoltaic System Using Boost

Converter

Shilpa Sreekumar

P G Scholar: Dept. of Electrical & Electronics Engineering

Amal Jyothi College of Engineering

Kottayam, India

Shilpasreekumar9@gmail.com

Anish Benny

Assistant Professor: Dept. of Electrical & Electronics

Engineering

Amal Jyothi College of Engineering

Kottayam, India

anishbenny@gmail.com

Abstract— Today, the energy crisis in the world has led to the

rise in use of renewable energy sources. With the advancement in

power electronic technology, the solar photovoltaic energy has

been recognized as an important renewable energy resource

because it is clean, abundant and pollution free. The efficiency of

the photovoltaic system may be increased by using Maximum

Power Point Tracker (MPPT). A number of algorithms are

developed to track the maximum power point efficiently. Most of

the existing MPPT algorithms suffer from the drawback of being

slow or wrong tracking. Introduction of intelligent MPPTs in PV

systems is very promising. This paper proposes an intelligent

control method for the maximum power point tracking (MPPT)

of a photovoltaic system under variable insolation conditions.

Here in this paper, intelligent control method uses a Fuzzy Logic

Controller applied to a DC-DC converter device. The result is

compared with the results obtained by using P&O method. The

effectiveness of proposed algorithm is validated by simulation

using Matlab/Simulink and is compared to those obtained by the

conventional methods. The result shows that the fuzzy logic

controller exhibits a better performance compared to that of

conventional method.

Keywords— Photovoltaic System, Maximum Power Point

Tracking, DC-DC Converter, P&O Algorithm, Fuzzy Logic

Controller, Artificial Intelligence.

I. INTRODUCTION

One of the major concerns in the power sector is the day to day increasing power demand but the unavailability of enough resources to meet the power demand using the conventional energy sources. The continuous use of fossil fuels has caused the fossil fuel deposit to be reduced and has drastically affected the environment depleting the biosphere and cumulatively adding to global warming [1]. Demand has increased for renewable sources of energy to be utilized along with conventional systems to meet the energy demand. Thus the growing demand on electricity, the limited stock and rising prices of conventional sources such as coal, petroleum etc has led to the use of renewable energy sources. Utilization of renewable energy resources is the demand of today and the necessity of tomorrow. With advancement in power electronic technology, the solar photovoltaic energy has been recognized

as an important renewable energy resource because it is clean, abundant and pollution free. The extraction of maximum available power from a photovoltaic module is called Maximum Power Point Tracking and is done by Maximum Power Point Tracking Controller. The efficiency of the photovoltaic system may be substantially increased by using Maximum Power Point Tracker (MPPT) [2]. A number of algorithms are developed to track the maximum power point efficiently. Among all MPPT methods, Perturb and Observe (P&O) method and Incremental Conductance method are most commonly used. Most of the existing MPPT algorithms suffer from the drawback of being slow tracking. Due to this the utilization efficiency is reduced.

II. MAXIMUM POWER POINT TRACKING

A typical solar panel converts only 30 to 40 percent of the incident solar irradiation into electrical energy. Maximum power point tracking technique is used to improve the efficiency of the solar panel. According to Maximum Power Transfer theorem; the power output of a circuit is maximum when the Thevenin impedance of the circuit (source impedance) matches with the load impedance. Hence the problem of tracking the maximum power point reduces to an impedance matching problem. In the source side, a boost converter is connected to a solar panel in order to enhance the output voltage so that it can be used for different applications like motor load. By changing the duty cycle of the boost converter appropriately, the source impedance can be matched with that of the load impedance [3].

The efficiency of a solar cell is very low. In order to increase the efficiency, methods are to be undertaken to match the source and load properly. One such method is the Maximum Power Point Tracking (MPPT) [2-3]. This technique is used to obtain the maximum possible power from a varying source. MPPT techniques are applied on various PV applications such as space satellite, solar vehicles and solar water pumping etc. Several methods are presented for maximum power point tracking (MPPT) from photovoltaic system such as Perturbation and Observation (P&O) method, Incremental Conductance method, Open Circuit Voltage

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method, Short Circuit Current method, Feedback of power variation with voltage technique, Feedback of power variation with current technique, Fuzzy Logic Controller based method etc [3-6].

In P&O method, the operating voltage is sampled and the algorithm changes the operating voltage in the required direction [7] [8]. The iteration is continued until the algorithm finally reaches the MPP [9]. This technique is simple to implement but the major drawback is the occasional deviation from the maximum operating point in case of rapidly changing atmospheric conditions [3-4][6]. The Incremental Conductance (Inc-Cond) algorithm is based on the fact that the slope of the curve power vs. voltage (current) of the PV module is zero at the MPP, positive (negative) on the left of it and negative (positive) on the right [10-11]. This method tracks rapidly under varying irradiation conditions more accurately than P&O method. It requires complex and costly control circuits [4-6].

Fuzzy Logic based controllers overcome the disadvantages of conventional methods in tracking maximum power point. Fuzzy Logic based controller is simple to implement gives better convergence speed and improves the tracking performance with minimum oscillations [12]. The FLC based MPPT controls the duty cycle of the dc-dc converter in standalone system using change in slope of P-V curve as input and change in voltage as output [13].

III. PROPOSED SYSTEM

The system consists of a PV panel connected to a boost converter to enhance and regulate the output voltage. It drives the DC load by using the power tracked from the solar panel. The MPPT controller is used to track the maximum power from solar panel. The block diagram of the proposed system is shown in Fig.1.

Fig.1 Block diagram of the proposed system

A. Photovoltaic System

Photovoltaic (PV) are solid-state, semi-conductor type devices which produce electricity when exposed to light. The word photovoltaic means "electricity from light." The building block of a solar panel is solar cell. A photovoltaic module is formed 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 modeled by utilizing a current source, a diode and two resistors. This model 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 constant

T : Cell working temperature

A : Diode ideal factor

Rsh : Shunt resistance

Rs : Series resistance

The characteristic equation of a solar module is

dependent on the number of cells in parallel and number of cells in series [18]. The current variation occurs is less dependent on the shunt resistance and is more dependent on the series resistance. Fig.3 shows the P-V, I-V curve of a solar panel. It can be seen that the cell operates as a constant current source at low values of operating voltages and a constant voltage source at low values of operating current [19].

F

Fig.3: P-V I-V curve of a solar cell at given temperature and solar irradiation

B. Boost Converter

A 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 DC sources, such as batteries, solar panels, rectifiers and DC generators. Since power must be conserved, the output current is lower than the source current.

Fig 4: Boost converter

Fig.4 shows the circuit diagram of a boost converter. Boost converter steps up the input voltage magnitude to a required output voltage magnitude without the use of a transformer. The main components of a boost converter are an inductor, a

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diode and a high frequency switch. These in a coordinated manner supply power to the load at a voltage greater than the input voltage magnitude. The control strategy lies in the manipulation of the duty cycle of the switch which causes the voltage change [20-21].

When the switch is closed and the inductor is charged by the source through the switch. The charging current is exponential in nature but for simplicity is assumed to be linearly varying [20]. The diode restricts the flow of current from the source to the load and the demand of the load is met by the discharging of the capacitor. When the switch is open and the diode is forward biased. The inductor now discharges and together with the source charges the capacitor and meets the load demands. The load current variation is very small and in many cases is assumed constant throughout the operation.

C. Maximum Power Point Tracking Controller

Maximum power point tracking controller used in this paper is Fuzzy Logic Controller. Fuzzy logic control is a convenient way to map an input space to output space. Fuzzy logic uses fuzzy set theory, in which a variable is a member of one or more sets, with a specified degree of membership.Recently fuzzy logic controllers have been introduced in the tracking of the MPP in PV systems. They have the advantage to being robust and relatively simple to design as they do not require the knowledge of the exact model. They do require in the other hand the complete knowledge 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. The fuzzy logic controller requires that each input/output variable which define the control surface be expressed in fuzzy set notations using linguistic levels. The process of converting input/output variable to linguistic levels is termed as Fuzzification. The behaviour of the control surface which relates the input and output variables of the system are governed by a set of rules. A typical rule would be–“If x is A THEN y is B” [16]. When all the rules are fired, the resulting control surface is expressed as a fuzzy set to represent the constraints output. This process is termed as inference. Defuzzification is the process of conversion of fuzzy quantity into crisp quantity. There are several methods available for defuzzification. The most commonly used is centroid method.

IV. PROPOSED FUZZY LOGIC CONTROLLER

Fuzzy logic is implemented to obtain the MPP operating voltage point faster and also it can minimize the voltage fluctuation afterMPP has been recognized. The proposed fuzzy logic based MPPT controller has two inputs and one

output. The error E(k) and change in error CE(k) are the input variables to Fuzzy Logic Controller and is given below in equation 2 & 3 for k

th 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 defined as 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 output variable.

Fig 6: Fuzzy logic controller

To design the FLC, variables which can represent the dynamic performance of the system to be controlled, should be chosen as the inputs to the controller. The input and output variables 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) have been 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 and output variables are shown in Fig.7(a), Fig.7(b) andFig.7(c) respectively. A fuzzy rule base is formulated for the present

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application and is given in table 1. The fuzzy inference of the FLC is based on the Mamdani’s method which is associated with the max-min composition. The defuzzification technique is based on the centroid method which is used to compute the crisp output.

TABLE I. FUZZY RULE TABLE

V. SIMULATION AND RESULTS

The PV module is modelled in MATLAB using equation 1 with the assumption that the PV module has constant temperature of 25

0C. 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 an insolation level of 1000W/m

2 and 25

0 C temperature is shown in Fig. 9

and Fig. 10 respectively. Electrical characteristics of the modelled PV are given in the Table 2.

TABLE II. ELECTRICAL CHARACTERISTICS OF PV CELL

The maximum power point tracking based on Fuzzy Logic

Controller and P&O algorithm were tested under varying solar irradiance and at a constant temperature of 25

0C. The simulink

model includes a PV model, a pure resistive load which is connected to the PV module through a dc-dc boost converter to enhance the output voltage from PV and MPPT controller to track maximum power. The sampling time taken for 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 irradiance and the ambient temperature. The performance of the proposed technique has been examined for variable solar radiance and at a constant temperature of 25

0C. The output produced by the

PV module is the PV current, which acts as a controlled current source for the input of the boost converter. The load resistance, R taken is 150 Ω. The MPPT block consists of MPPT algorithm, namely Fuzzy Logic Controller and P&O algorithm. The output of the MPPT controller is given to discrete PWM generator for pulse width modulation. The switching frequency, fs was set to be 25 kHz in this simulation. Fig.11 (a) and Fig. 11(b) show the configurations of MPPT algorithms in Simulink using Fuzzy Logic Controller and P&O algorithm.

Fig.12 and Fig.13 shows the power response obtained using Fuzzy logic controller based MPPT and Perturb & Observe algorithm. From the above results it seems that the PV power which is controlled by the proposed Fuzzy Logic Controller is more stable than the conventional MPPT techniques. The power curve obtained with FLC is smoother when compared to P&O algorithm. Fig.14 and Fig.15 shows

E

CE

NB NS ZE PS PB

NB ZE ZE PB PB PB

NS ZE ZE PS PS PS

ZE PS ZE ZE ZE NS

PS NS NS NS ZE ZE

PB NS NB NB ZE ZE

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

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the output voltage of boost converter using Fuzzy Logic Controller.

Fig. 12 Power response obtained using fuzzy logic controller

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

VI. CONCLUSION

Two MPPT control algorithms, namely Fuzzy Logic Controller and P&O were discussed and reviewed. The study presents a simulation comparison of the maximum power produced for Fuzzy Logic Controller based method and P&O algorithm. The P&O algorithm is the simplest method, which results in low cost of installation and it may be competitive with other MPPT algorithms. On the other hand, the FLC method provides a simpler way to arrive at a definite conclusion, but it highly depends on the user’s knowledge of the process operation for the parameter setting of Fuzzy Logic Controller. The conventional MPPT algorithms are not capable of tracking maximum power rapidly under varying atmospheric conditions.

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