A novel particle swarm optimization algorithm for maximum power point tracking based photovoltaic system

Abstract

In recent years, solar energy is effectively utilized as an alternate energy source for generating electricity. Maximum Power Point Tracking(MPPT) is applied to the photovoltaic (PV) system to extract maximum power (MP). Numerous algorithms are developed and implemented to extract the MP under varying environmental conditions. One such algorithm is the Particle Swarm Optimization algorithm(PSO).This article introduces a novel PSO algorithm with Cauchy distribution to track MP from the PV system. It is designed to overcome the drawback of slow convergence rate of conventional PSO. Parameters required for conventional PSO are inertial weight, acceleration coefficients, and a number of particles. In case of Cauchy PSO(CPSO), tuning parameter is the number of particles. In order to increase the convergence speed, Cauchy distribution is used instead of normal distribution function to generate the random numbers. The advantage of this algorithm is that it provides the global best-optimized result at a faster convergence speed. It has the ability to track the MP in extreme climatic conditions with varying loads. The proposed method outperforms the standard PSO and some of the existing methods in terms of quick convergence and also has a simplified structure.