FUZZY LOGIC BASED MAXIMUM POWER POINT TRACKER FOR PHOTO VOLTAIC SYSTEM

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1 286 FUZZY LOGIC BASED MAXIMUM POWER POINT TRACKER FOR PHOTO VOLTAIC SYSTEM K Padmavathi*, K R Sudha** *Research Scholar, JNTU, Kakinada, Andhra Pradesh, India ** Professor, Department of Electrical Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India ABSTRACT Photovoltaic system exhibits nonlinear characteristics and maximum power (MP) points that changes with solar insolation and cell s temperature. The Photo Voltaic System can supply the maximum power to the load at a particular operating point which is generally called as maximum power point (MPP), at which the entire system operates with maximum efficiency and produces its maximum power. The Maximum Power Point Tracker control technique based on fuzzy controller is implemented to generate the optimal voltage from the photovoltaic system by modulating the duty cycle applied to the boost dc-dc converter. The efficacy of fuzzy controller is compared with perturb and observe method. Keywords Photo Voltaic System, Maximum Power Point Tracking, Perturb & Observe method, Fuzzy Controller, Boost Converter I. INTRODUCTION Renewable energy resources play a vital role in the generation of electricity. The sources of renewable energy are Solar, Wind, Hydro power, Biomass, Geothermal, Hydrogen and Ocean. Solar energy is directly converted into electrical energy by using Photovoltaic (PV) cells[1]. Because of no fuel cost, pollution free, little maintenance and emitting no noise, Photovoltaic (PV) is a good source of energy [2]. The PV system exhibits a nonlinear V-I characteristics and its maximum power (MP) point fluctuate with the change in temperature and solar insolation. Most of the research work is going on in this area to track the maximum power point. The Maximum Power Point Tracking (MPPT) is applied to PV systems to extract maximum available power from sun at all solar insolations. Many MPPT techniques have been proposed by implementing different control strategies [3]. A DC to DC converter serves the purpose of transferring maximum power from the PV system to the load. Different types of conventional methods to obtain maximum power point are constant voltage method, Open Circuit Voltage method (OCV), Short Circuit Current method (SCC), Perturb and Observe method (P&O), Incremental Conductance method (INC) [4]. Compared with conventional methods, Artificial Intelligence (AI) techniques gives fast response under any operating conditions with accurate results [5]. This paper presents the AI technique using Fuzzy Logic Controller (FLC) to implement MPPT in order to improve energy conversion efficiency and also compared with P&O method. II. PHOTO VOLTAIC SYSTEM 2.1 SYSTEM DESCRIPTION The dispersion of Photo Voltaic (PV) is very fast because it generates electricity in a clean, quiet and reliable manner. PV system is a static electricity generator as it produces electricity when the photon of the sunlight strikes the semiconductor materials in the PV cell. Individual PV cells are interconnected in a sealed, weatherproof package which is known as a module [6]. To increase the voltage modules are connected in series and to increase the current they are connected in parallel. The PV modules are connected in series and parallel which is known as PV array to achieve the desired output. Fig. 1 shows the cells, modules and array of photovoltaic System. PV systems can be easily expanded by adding more modules and manufactured to withstand the most rugged conditions. The benefits of using photovoltaic system are (i) operate with little maintenance, (b) pollution free, (c) reduced production end wastes and emissions. The installation of photovoltaic system requires high initial capital cost, but the operating cost is low compared with obtainable power technologies. The installation of photovoltaic system requires high initial capital cost, but the operating cost is low compared with obtainable power technologies.

2 287 The photovoltaic system can be modeled mathematically as given in equations (1) - (4) The module photo current Iph depends is given by Where I scr is short circuit current of PV module; K i is the short circuit temperature coefficient; T is the operating temperature of module in Kelvin; T r is the reference temperature in Kelvin; G is the solar radiation in watt/mt 2. The reverse saturation current of the module is (1) (2) Figure 1 Cells, Modules and array of PV system 2.2 EQUIVALENT CIRCUIT AND MATHEMATICAL MODELLING Photovoltaic cells consist of a silicon P-N junction that when exposed to light releases electrons around a closed electrical circuit. From this premise the circuit equivalent of a PV cell can be modeled through the circuit shown in Fig. 2. Electrons from the cell are excited to higher energy levels when a collision with a photon occurs. These electrons are free to move across the junction and create a current. This is modeled by the light generated current source. The intrinsic P-N junction characteristic is introduced as a diode in the circuit equivalent. Figure 2 Equivalent circuit of PV cell The current source Iph represents the cell photocurrent. Rp and Rs are the intrinsic shunt and series resistances of the cell, respectively. Rp is inversely related with shunt leakage current to the ground.the PV efficiency is insensitive to variation in Rp but a small variation in Rs will affect the PV output power. Where q is the electron charge; V oc is the open circuit voltage; N s is number of series cells; k is Boltzmann s constant, A is fitting factor The module saturation current depends on the cell temperature, which is given by Equation 3. Where E g is the band gap energy of semiconductor. The output current of PV module is given by (4) (3) Where N p is the number of parallel cells; R s & R P are series and shunt resistances of the module respectively. The output power of the photo voltaic array is given Equation 5 (5) Fill Factor(FF) is defined as the ratio of the maximum obtainable power of the photo voltaic cell to the product of open circuit voltage(v oc )and short circuit current (I sc ) [7]. V oc and I sc are the maximum values of the pv cell. Graphically, the FF is a measure of the "squareness" of the solar cell and is also the area of the largest rectangle which will fit in the IV curve. (6) Where V mp and I mp are the voltage and current at maximum power point. V oc is the open circuit voltage

3 288 and I sc is the short circuit current. Table 1 shows the parameters of photovoltaic system. Table1. Photovoltaic System Parameters PV array parameters Symbol Values Reference Temperature T r 298K Solar irradiance G 1000 watt/mt 2 Temperature coefficient K i A/K Cell short-circuit I scr 3.75A current at T r Saturation current at I or A reference temperature Boltzmann s constant K e-23J/ K Charge of an electron q e-19C Fitting factor A 1.3 No. of parallel cells N p 6 No. of series cells N s 10 III. MAXIMUM POWER POINT TRACKING PV system s efficiency depends on Maximum Power Point Tracking (MPPT). Because of the nonlinear characteristics of output voltage and current with change in solar radiation, operating temperature and load current, the efficiency of PV system is less [8]. To overcome these problems, the maximum power point is tracked. Fig. 3 shows the typical I-V and P-V curves of photovoltaic system. A PV Module produces its maximum current when there is no resistance in the circuit, i.e. a short circuit between its Positive and Negative terminals. This maximum current is known as the Short Circuit current (I sc ). The voltage in the circuit is zero when the PV Module is shorted. The maximum voltage occurs when there is a break in the circuit and is known as Open Circuit voltage (Voc). Under this condition, the resistance is infinitely high and there is no current. The point on the knee of the I-V Curve where the maximum power output is located is called the Maximum Power Point (MPP). The voltage and current at this Maximum Power Point are designated as Vmp and Imp. In this paper MPPT using perturb & observe method is compared with MPPT using fuzzy logic controller. Figure 3 I-V and P-V curves of Photo Voltaic System 3.1 PERTURB & OBSERVE METHOD This is a technique used to obtain the Maximum Possible Power (MPP) from a varying source. Perturb and Observe (P&O) method is implemented. In this algorithm a small perturbation is introduced in the system. Due to this perturbation, the power of the module alters then the perturbation is carried in that direction. When the maximum power is achieved, the power instant reduces and the perturbation reverses. Fig. 5 represents the flowchart of MPPT technique. The drawback of P&O method is it oscillates around the MPP in steady state operation and for rapidly increasing or decreasing irradiance levels, it can track in the wrong direction [9]. Figure 4 Perturb & Observe Algorithm

4 FUZZY LOGIC MPPT Fuzzy Logic Controller (FLC) can be implemented as a Maximum Power Point Tracking (MPPT) controller to achieve the maximum power from the photo voltaic system [10]. The input variables are PV current and voltage and the output variable is duty cycle. Error (e) and change of error (ce) are calculated using the following equations. (7) (10) Where I pv and V pv are photo voltaic current and voltage, t and (t-1) are actual and previous states. The linguistic variables of both input and output membership functions are NB(negative big), NM(negative medium), NS(negative small),ze(zero), PS(positive small), PM(positive medium), PB(positive big) are represented in Fig. 5. (8) (9) ZE PS ZE ZE ZE NS PS NS NS NS ZE ZE PB NB NB NB ZE ZE IV. BOOST CONVERTER Boost Converters are DC to DC converters used for converting low voltage to high voltage. It consists of inductor, capacitor and switches. DC-DC converters play a role of charge controller, MPP trackers and PV interface with load. The converter consists of semiconductor switch, inductor and a capacitor. The circuit diagram of a boost converter is shown in Fig. 6. When SW1 is switched on, input charges the inductor. When SW1 is switched off, the source voltage and inductor together charges the capacitor to a higher value greater than source voltage. Diode avoids the discharge of capacitor when SW1 is in on state. Figure 6 DC-DC Boost converter V. RESULTS & DISCUSSIONS The analysis of Photo Voltaic system interfaced to boost converter has been done using MATLAB software using SIMULINK. The simulink was evaluated for SOLAREX MSX-60 and the specifications are shown in Table 3. Table 3 Parameter specification of SOLAREX MSX-60 Figure 5 Membership functions of input and output variables The output variable, d is the pulse width modulation signal to produce the switching pulses of the DC to DC converter. The fuzzy logic controller s rule base used in PV system is represented by Table 2 Table 2 Rule base of Fuzzy logic MPPT e/ce NB NS ZE PS PB NB ZE ZE PB PB PB NS ZE ZE PS PS PS Maximum Power(P max ) 60W P max 17.1V P max 3.5A Guaranteed minimum P max 58W Short circuit current(i sc ) 3.8A Open circuit voltage (V oc ) 21.1V The comparative performance of Perturb & Observe MPPT and Fuzzy MPPT at a solar radiance of 1000watts/mt 2 and at an operating temperature of 25 C is presented in this paper. Fig. 7 shows results comparing the output voltage of boost converter using

5 290 P&O and Fuzy MPPT controllers. The system with Fuzzy controller settles faster than P&O technique. Figure 7 Comparison of DC voltage using P&O and fuzzy MPPT controllers The current-voltage (I-V) and power-voltage(p-v) characteristics depend on the variation of solar irradiance and operating temperature. The observations are undertaken for an operating temperature of 25 C, 40 C, 50 C, 60 C and 80 C at a constant solar irradiance of 1000watts/mt 2. Fig.8 and Fig. 9 represents the I-V and P-V characteristics of the PV system at various temperatures. It is observed that the current is maximum and also having almost constant value at the lower voltage range. From P-V characteristics it is observed that voltage and power of the PV system reduces with increase in operating temperature. Figure 9 P-V characteristics of PV module for varied temperatures The system considered is also simulated for various solar irradiances with a constant temperature. The observations are also undertaken for solar irradiance of 200W/mt 2, 400W/mt 2, 600W/mt 2, 800W/mt 2 and 200W/mt 2 at a constant temperature. Fig. 10 & Fig. 11 shows the I-V and P-V characteristics for various solar irradiances. Figure 10 I-V characteristics of PV module for varied solar irradiances Figure 8 I-V characteristics of PV module for varied temperatures Figure 11 P-V characteristics of PV module for varied solar irradiances

6 291 The Fill Factor (FF) evaluates the performance of PV system. In Table 4 resultant data for various solar irradiance by implementing Fuzzy logic MPPT controller for the photo voltaic system are tabulated. Table 4 The resultant data of proposed PV module at various solar irradinces. Solar W/ Irradiance mt 2 Maximum W Power Open V Circuit Voltage Short A Circuit Current Fill Factor VI. CONCLUSION MPPT controllers are implemented to ensure that PV system operates at its maximum power point. These controllers minimize the error between the operating power and the reference maximum power which is variable according to the load and of the weather conditions. In this paper, MPPT controller using Fuzzy is compared with Perturb & Observe method. The fuzzy MPPT is better than the P&O method. [5] Aurobinda Panda, M. K. Pathak, S. P. Srivastava, Fuzzy Intelligent Controller for The Maximum Power Point Tracking of a Photovoltaic Module at Varying Atmospheric Conditions, Journal of Energy Technologies and Policy, 1(2), 2011, [6] M. G. Villalva, J. R. Gazoli, and E. R. Filho, Comprehensive approach to modeling and simulation of photovoltaic arrays, IEEE Transactions on Power Electronics, 24(5), 2009, [7] Pradhan Arjyadhara, Ali S.M, Jena Chitralekha, Analysis of solar PV cell performance with changing Irradiance and Temperature, International Journal of Engineering and Computer Science, 2(1), 2013, [8] Ali Reza Reisi a,n, Mohammad Hassan Moradi b, Shahriar Jamasb b, Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review, Renewable and Sustainable Energy Reviews, 19, 2013, [9] D. P. Hohm, M. E. Ropp, Comparative Study of Maximum Power Point Tracking Algorithms Using an Experimental, Programmable, Maximum Power Point Tracking Test Bed, /00,IEEE, 2000, [10] M. M. Algazar, H. Al-Monier, H. A. El-Halim, and M. E. E. K. Salem, Maximum power point tracking using fuzzy logic control, International Journal of Electrical Power and Energy Systems, 39(1), 2012, REFERENCES [1] M G. Villalva, J. R. Gazoli, E. Ruppert F, Comprehensive approach to modeling and simulation of photovoltaic arrays, IEEE Transactions on Power Electronics, 25(5),2009, [2] Masato Oshiro, Kenichi Tanaka, Tomonobu Seniyu, Shohei Toma, Atsushi Yona, Ashmed Yousuf Saber, et al.,optimal voltage control in distribution systems using PV generators, International Journal of Electrical Power and Energy System,33(3),2011, [3] Ali Reza Reisi a,n, Mohammad Hassan Moradi b, Shahriar Jamasb b, Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review, Renewable and Sustainable Energy Reviews,19,2013, [4] P. Hohm and M. E. Ropp, Comparative Study of Maximum Power Point Tracking Algorithms, Prog. Photovolt: Res. Appl., 11, 2003,