Enhancing the performance of a maximum power point tracking system using an improved particle swarm optimization method

This thesis introduces a novel approach to improve the efficiency of maximum power point tracking (MPPT) systems for solar panels, dealing with both uniform and non-uniform irradiation scenarios. The proposed system employs the Particle Swarm Optimization (PSO) algorithm due to its ease of implementation and proficiency in identifying global optima, particularly in scenarios involving partial shading. This study builds upon studying prior algorithms, notably PSO, by devising inventive solutions to overcome prevailing challenges in these approaches. Within this thesis, an innovative methodology is outlined, involving the dynamic adjustment of PSO algorithm parameters during execution. This dynamic adaptation seeks to strike a balance between speed and system efficiency. Moreover, a meticulous calibration of parameters governing the interaction between the converter and the PSO algorithm is performed. This calibration aims to ensure seamless data exchange between the components, fostering stability and minimizing fluctuations within the MPPT system. The devised system is designed and evaluated through MATLAB Simulink. The outcomes empirically substantiate the system's efficacy in capturing peak power with high speed and efficiency across a diverse array of irradiation conditions.