Investigation of the effect of perturb and observe, genetic algorithm and particle swarm optimization algorithms on photovoltaic system efficiency

Photovoltaic power conversion systems are among the most common renewable energy sources worldwide, based on environmentally friendly electrical energy production from solar radiation. Power systems basically consist of solar panel array, battery pack, charge regulators and converters. New technology solar chargers use technologies including Pulse Width Modulation (PWM) and Maximum Power Point Tracking (MPPT) technology to charge the battery pack as efficiently as possible. MPPT technologies operate at higher efficiency than systems using the PWM method. Looking at the working principle of MPPT technology, MPPT depends on its power-voltage characteristics and determines the ideal charging current to be used to charge the battery bank. In response to this, the MPPT method attracts the attention of researchers, especially in the field of development and increasing its efficiency. Perturb and observe method (P&O) algorithm has become very popular in the field of MPPT. In order to reach MPP, it is necessary to research the features of the solar panel step by step. In this study, we propose a method to make MPPT based on search algorithms, namely genetic algorithm (GA) and particle swarm optimization (PSO), which are basically used in optimization processes. The optimization algorithms used are based on the generated array elements to find the maximum available values by performing a random search at the starting point. MPPT, P&O, GA and PSO algorithms based on modeling and simulation in MATLAB/SIMULINK environment are compared. The results show that the PSO method significantly increases the efficiency of the system in the use of ideal search algorithms compared to the traditional algorithm.