Abstract
This article demonstrates maximum power point tracking (MPPT) using a DC-DC boost converter. It introduces an intelligent control technique with fuzzy-based pattern search (PS) optimization for the MPPT controller, enhancing energy conversion efficiency. The fuzzy-PS approach is further refined with PA optimization. A comprehensive performance evaluation compares it with various optimization algorithms. The controller is tested under changes in irradiance and temperature, showing its performance against the Perturb and Observe (P&O) algorithm. The fuzzy controller is optimized to provide the best membership functions (MFs) using PS optimization, particle swarm optimization (PSO), and genetic algorithm (GA), with root mean square error (RMSE) as the objective function. PS optimization outperforms other algorithms. The fuzzy-PS optimization achieves the lowest RMSE of 0.6861 after 100 iterations, while fuzzy-GA and fuzzy-PSO reach RMSEs of 1.257 and 0.9454, respectively. The proposed fuzzy-PS MPPT controller effectively adapts to irradiance and temperature variations, achieving maximum power outputs up to 74.48 kW and Comparative evaluations revealed an average MPPT efficiency of 99.7%, demonstrating superior tracking performance compared to the P&O algorithm.