Comparative analysis of photovoltaic generating systems using particle swarm optimization and cuckoo search algorithms under partial shading conditions

Abstract

The energy demand on the electricity grids increased rapidly due to that non-conventional energy sources (NCES) like PV, wind power plants are encouraged to establish and operate with the grid. Out of the available NCES, Photovoltaic generating systems (PVGS) are widely penetrated to the grids. As the output power extracted from the PVGS is non-linear, it becomes fluctuating depending on the available Irradiance (G), Temperature (T), and partial shading conditions (PSC). So, there is a need for the development of maximum power point tracking (MPPT) algorithms in the PVGS for maximizing the output power and minimizing the fluctuations. In this article, a comparative analysis of two advanced MPPT algorithms namely particle swarm optimization algorithm (PSOA) and cuckoo search algorithm (CSA) is presented. These two algorithms are used to control the duty cycle of the boost converter to maximize the PVGS output power. The proposed design is modeled using Matlab/Simulink software and the results were obtained and analyzed.