An Effective AFNIS-MPPT-Based Method for Optimizing Hybrid Energy Harvesting Systems

This paper proposes the use of advanced Maximum Power Point Tracking (MPPT) techniques to enhance the efficiency of Hybrid Renewable Energy Systems, with a particular focus on solar and wind energy sources. As global demand for renewable energy continues to rise, effective control strategies are essential for optimizing power generation under variable environmental conditions. Among the various MPPT methods, Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Logic Control (FLC) have demonstrated significant potential in improving system performance. ANFIS, in particular, leverages adaptive algorithms and membership functions to minimize tracking errors and consistently maintain operation at the Maximum Power Point (MPP). Simulation results confirm that ANFIS outperforms traditional approaches, delivering smoother output curves, higher voltage levels, and increased power extraction, especially in wind energy applications. These outcomes underscore the viability of ANFIS-based control strategies in enhancing the reliability and efficiency of renewable energy systems, supporting their seamless integration into conventional power grids, and advancing global sustainability efforts.

Keywords: Fuzzy logic control, hybrid energy harvesting, maximum power point tracking, renewable energy sources, Adaptive Neuro Fuzzy Interface System.