Maximum Power Point Tracking for a Grid Connected Photovoltaic System using Sliding Mode Control

Abstract

This paper presents a method to track the maximum power point for an isolated grid connected photovoltaic system. The method used to achieve this goal is sliding mode control. A high frequency flyback converter topology working in continuous conduction mode is used to boost the voltage and also provides galvanic isolation between input and output side. An inverter is used to invert the power for a grid connected operation. Therefore, the primary objective of this study is to design a sliding mode controller which can track maximum power driving a high frequency flyback converter and demonstrate its practicality as a highly efficient maximum power point tracker. This system is modelled and tested in MATLAB SIMULINK. To verify the results a practical implementation of sliding mode controller with high frequency flyback transformer is performed in a hardware setup. Keyword: Flyback converter INTRODUCTIONMost of the world's electricity demand is fulfilled by fossil fuels and nuclear plant and very small amount is met through renewable energy resources such as photovoltaic or solar energy. Due to increase in pollution from the conventional plants and depleting sources has led to increase use of photovoltaic energy as a source of electricity. But the success of a PV plant depends on weather conditions because the output of a solar cell depends on solar irradiation and temperature which are not constant and changes with time and season. There are various conventional methods to track maximum power point such as perturb and observe (P&O) and incremental conductance. The P&O algorithm is the simplest algorithm and is easy to implement among all the algorithms. It tracks the MPP by measuring the rate of change of power with respect to voltage at every point, being zero at the MPP, positive on the left of the MPP, and negative on the right. But it has various disadvantages, such as poor tracking, less efficient during changing weather conditions, inability to track MPP during low irradiance, oscillations around MPP and slow response [1]. Incremental conductance shows better performance than P&O algorithm and track MPP by comparing the incremental and instantaneous conductance with less oscillation around MPP and faster response [2]. Fuzzy logic does not require an accurate mathematical model and can work with inexplicit input. It can also handle non-linearity in a system. It mainly has four basic step fuzzyfication , rule base, inference engine and De-Fuzzyfication. The output of FLC is the reference voltage which is generated by the change in voltage and change in current at a sampling time K from the solar panel. The reference voltage in turn generates the error signal, based on which duty cycle is generated. The only disadvantage of the FLC is that it is very difficult to formulate the