International Conference on Renewable Energies and Power Quality (ICREPQ’13) Bilbao (Spain), 20 th to 22 th March, 2013       (RE&PQJ) ISSN 2172-038 X, No.11, March 2013 An Efficient Fuzzy Logic Based Maximum Power point Tracking Controller for Photovoltaic Systems Muhammad Sheraz and M.A.Abido Department of Electrical Engineering King Fahd University of Petrolium and Minerals Dhahran, 31261 (Saudi Arabia) Phone/Fax number: +966 3 860 1681/+966 3 860 3535, e-mail: isheraz@kfupm.edu.sa, mabido@kfupm.edu.sa Abstract. This paper represents a Fuzzy Logic (FL) based Maximum Power Point Tracking (MPPT) controller for a PV array. The proposed controller is aimed at adjusting the duty cycle of the DC-DC converter switch to track the maximum power of a PV array. MATLAB/Simulink is used to develop and design the PV array system equiped with the proposed MPPT controller. The developed model has been examined under different operating conditions. The performance of the proposed controller has been compared with conventional ones. The results show that the proposed controller is able to track the MPP in a shorter time with less fluctuations. In addition, the robustness of the proposed controller has been confirmed in the rapidly changing irradiation conditions. Key words PV system, maximum power point tracking, MPPT, DC- DC boost converter, Fuzzy Logic 1. Introduction Solar energy is the most abundant and environmental friendly RES and can be converted to electrical energy directly using the photovoltaic arrays. PV arrays have a non-linear I-V and P-V characteristics and have one optimum point called Maximum Power Point (MPP). This MPP is highly vulnerable to the ambient conditions, that are irradiation and cell temperature, and these conditions are always changing with time which keep varing the MPP. Therefore the maximum power point tracking (MPPT) controller is of the great importance and are coupled with the PV arrays to track the MPP and extract maximum possible power from the array. Maximum power point tracker works with the DC-DC converter which is operated as an interface between the PV panel/array and load/inverter. DC-DC converter performs two major tasks, one is to track the maximum power point and to regulate and step up or step down the output voltage. Voltage from the PV panel, which is varying depending on ambient conditions, is given as input to the DC-DC converter and its output is constant voltage across the capacitor where load/inverter can be connected. MPPT works as a controller for the DC-DC converter and controls the duty ratio of the switch such that it tracks the MPP under the changing ambient conditions. The idea of MPPT is not new, many MPPT methods have been proposed by researchers to improve the tracking efficiency. These techniques differ in sensor required, complexity, cost, and convergence speed [1]-[3]. In [4] and [5] fractional open circuit voltage method is implemented that based on the fact that the ratio of the maximum power voltage (V MP ) and the open circuit voltage (V OC ) are approximately linearly proportional under varying weather conditions. The yielded power from PV panel definitely is less than the real power at MPP because of the obvious reason that this method is based on the approximation. Following the same pattern fractional short circuit current method is shown in [6] which uses the fact that the ratio of maximum power current (I MP ) and short circuit current (I SC ) are linearly proportional. This method has the same drawbacks and weakness as that of fractional open circuit voltage method. Perturb and Observe (P&O) method [7], [8] and Hill climbing method [9] are most popular because of their simplicity and low cost. Both the methods work on the same principle of perturbing the PV system and observing its effect on the PV panel power output. Difference lies in the method of perturbation, in P&O panel output voltage/current is perturbed while in Hill climbing duty cycle of DC-DC- converter is perturbed. Incremental Conductance (InCond) method is used in [10] to MPP tracking. All these methods may fail to track MPP in rapidly varying atmospheric conditions and have oscillations in the steady state which can be reduced by decreasing the perturbation size but at the expense of tracking speed [3], [11]. Many modifications have been employed in P&O and InCond by researchers but cannot overcome the shortcoming thoroughly [11]-[16]. In recent years some Artificial Intelligence (AI) techniques like Artificial Neural Network (ANN) [17] and Fuzzy Logic [18] have been implemented to prevail over these problems. The fuzzy-logic controller (FLC) based MPPT has been proposed in [19]-[22] to overcome the shortcoming of the conventional algorithms. All proposed FLC in the literature have the same output that is change https://doi.org/10.24084/repqj11.242 146 RE&PQJ, Vol.1, No.11, March 2013