Optimal Hybrid Neuro-fuzzy Based Controller Using MOGA for Photovoltaic (PV) Battery Charging System

摘要： This paper proposes an optimal hybrid neuro-fuzzy/fuzzy controller based on maximum power point tracking (MPPT) technique and voltage regulation for photovoltaic lead-acid battery charging system through the constant current and constant voltage (CC-CV) charge, denoted by NFC-CC/FLC-CV. The parameter optimization of NFC and FLC, including rule selection, based on multi-objective genetic algorithm (MOGA) is applied to the NFC-CC design to improve the tracking accuracy while reducing complexity. By means of genetic optimization, the number of fuzzy rules can be greatly reduced by 50%. In addition, GA is applied to the FLC-CV design to increase voltage regulation (VR) accuracy. After satisfying the stability condition through the solutions determined by MOGA and GA, the performances of controllers under rapidly-changing weather are evaluated tradeoff by several creteria, including transient response, stabilized accuracy, charging time, and energy utilization and charging ef?ciency. As results, the proposed controller outperforms the other existing controllers with the fastest rise time without overshoot, the highest MPPT and VR accuracy with negligible oscillations, a 12-23% reduction in charging time, and an increase of 5-15% and 1-6% in energy utilization and charging ef?ciency. Furthermore, it provides superior results in terms of computational complexity by achieving the minimum number of multiplications and system parameters, and high reliability with the lowest Akaike information criterion (AIC).