Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

Abstract

Solar energy is one of the most promising renewable energy resources. Over the last few decades, photovoltaic (PV) systems have grown in popularity. Since the maximum power point (MPP) of a solar system changes with environmental circumstances, the maximum power point tracking (MPPT) technique is required to get the most power out of the solar system. Various MPPT techniques based on classical and artificial intelligence (AI) methodologies have been proposed in the literature so far. In this paper, we aim to provide a thorough comparative analysis of the most widely used MPPT algorithms based on AI. The MPPT techniques discussed are based on fuzzy logic (FL), artificial neural networks (ANN), and the suggested hybrid approach ANN-fuzzy. The designed MPPT controllers are evaluated in the same PV system, which consists of a PV module, a DC-DC boost converter, and a DC load, under the same weather profile. Using the MATLAB/Simulink simulation tool, the tracking accuracy, response time, overshoot, and steady-state ripple of each method are tested in different weather conditions. The simulation results show that the ANN-fuzzy proposed tactic outperforms both the FL and the ANN MPPT controllers in correctly and successfully tracking the maximum power under diverse atmospheric conditions.