Enhancing efficient solar energy harvesting : A process-in-loop investigation of MPPT control with a novel stochastic algorithm

Abstract

PV systems currently generate 4% of the world's energy needs, and their share is growing quickly. The maximum power point tracking (MPPT) is a complex non-convex optimization problem because the electrical characteristics of the PV model are nonlinear. Changes in temperature, partial shading (PS), and irradiance levels can all affect the amount of power that can be extracted from the solar system. Therefore, in this work, a novel energy valley optimizer (EVO) based MPPT algorithm is suggested to extract maximum power from solar. The classical perturb and observe (P&O), whale optimizer algorithm (WOA), cuckoo search algorithm (CSA), and particle swarm optimization (PSO) algorithms are all compared to EVO. Five case studies, including a field atmospheric data study, partial shading, variable temperature, and irradiance, are used to conduct in-depth analytical and statistical analysis. Furthermore, the successful verification of the MPPT control algorithm on the real microcontroller (Arduino MKRZERO board) through the PIL test is a critical milestone in this research. Quantitative, comparative, statistical and experimental results indicate that the proposed EVO-based MPPT achieves superior performance through 30% quicker tracking time and 80% faster settling time, which result in 4–8% higher power efficiency. The results indicate that the suggested MPPT controller successfully addresses the shortcomings of the current MPPT methods.