Photovoltaic MPPT Performance Adaptability to Partial Shading Resilience and Load Variations With Modified Adaptive Jaya Optimization

Solar Photovoltaic (PV) power is the most common and efficient renewable energy source. The potential for utilizing solar PV energy on Earth is enormous. PV module characteristics significantly influence extracting maximum output from current solar power installations. Solar insolation and panel temperature substantially influence the voltage-current (V~I) and power-voltage (P~V) properties. The degree to which the system’s efficiency may be improved is determined by the accuracy with which the maximum power point tracking (MPPT) controller follows the nonlinear PV characteristics. This comprehensive investigation delves into the solar PV system dynamics, highlighting how modules induce power loss because of partial shading-induced peaks. Modified Adaptive Jaya Optimization (MAJO) is a novel meta-heuristic optimization approach designed to monitor maximum global power. MAJO boasts advantages over the conventional Deterministic Jaya Optimization (DM-Jaya) method under partial shading configurations (PSCs), exhibiting quicker convergence times and requiring fewer computing operations. MATLAB/Simulink and experimental scenarios are simulated and assessed under various environmental conditions, including step changes in solar irradiance, partial shading, and load variation, to evaluate the effectiveness of the proposed MAJO algorithm. The acquired findings are compared with the DM-Jaya and the Modified particle swarm optimization (MPSO), Maximum power trapezium (MPT) and Voltage window search (VSW) suggesting and demonstrating that the proposed MAJO algorithm yields better MPPT performance. Rigorous testing with a prototype experimental setup yielded a remarkable 99.9% tracking efficiency for the studied PSCs, accompanied by reduced settling times of 0.6s and convergence time of 0.154s. Additionally, the efficacy of the MAJO in terms of faster convergence characteristics, and attaining the optimal solution has been investigated on several unconstrained benchmark functions.