INVESTIGATIONS ON DIFFERENT GLOBAL PEAK TRACKING ALGORITHMS FOR EXTRACTING MAXIMUM POWER FROM PHOTOVOLTAIC SYSTEMS

Abstract

Photovoltaic (PV) systems have been emerged as promising source of energy. The optimal operation of a photovoltaic (PV) system is essential for maximizing power extraction from the PV source. To achieve this, a maximum power point tracking (MPPT) algorithm is employed in conjunction with a DC-DC converter. However, two key challenges arise when using a converter: (1) locating the maximum power point (MPP) and (2) ensuring reliable performance of the PV model under varying weather conditions. Therefore, the design of the converter plays a crucial role. This research focuses on two objectives related to non-isolated boost converters. Firstly, it involves determining the values of specific parameters such as input capacitor, output capacitor, and inductor. Secondly, it employs state-space modeling with averaging techniques to derive governing equations. Secondly, Identifying the different global peak tracking (GPT) algorithms for tracking peak power of PV array along with their comparison. Various MPPT methods have certain merits and demerits. Under non-uniform shading conditions, which involves change in environmental conditions like irradiance, many tracking techniques abort to track the global peak power (GPP) under partial shading conditions (PSC). Therefore, investigations are done under non uniform shading conditions considering conventional MPPT techniques compared with the modified MPPT algorithms to highlight the performance of different techniques. Simulation results comparing the effectiveness of the conventional Particle Swarm Optimization (PSO), Adaptive PSO and Hybrid PO-PSO, Modified Hybrid PO & PSO have been done and it indicates that algorithms discussed here achieve global peak. A simulation model for 250W and 500 W maximum power with a practical boost converter along with MPPT controller is used for investigation using MATLAB Simulink.