A Hybrid Control Method for Maximum Power Point Tracking (MPPT) in Photovoltaic Systems

Solar photovoltaic (PV) energy has witnessed growth in the past decade. Nowadays, PV energy systems have proved to be effective methods for renewable energy resources with minimum environmental impacts. Due to these environmental and economic benefits, PV systems are being widely deployed as distributed energy resources in distribution generation systems or microgrids. Maximum power point tracking (MPPT) algorithms have an important role to play due to optimization performance in these systems. In this paper, PV array output voltage has been optimized by increasing the MPPT algorithm performance. A new hybrid fuzzy-neural MPPT controller is proposed. Training data in neural network are optimized by genetic algorithm. The proposed controller is simulated and studied using MATLAB software. The obtained results show superior capability of the suggested method in MPP tracking under rapid fluctuation of atmospheric conditions and converter load.