Global Maximum Power Point Tracking Based on Intelligent Approach for Photovoltaic System Under Partial Shading Conditions

This paper presents the design of a controller for Maximum Power Point Tracking (MPPT) of a photovoltaic system. The proposed controller relies upon a Recurrent Neuro-Fuzzy (RNF) which is designed as a combination of the concepts of Sugeno fuzzy model and neural network. The controller employs the RNF of four-layer with sixty-four fuzzy rules. Moreover, for the proposed RNF an improved self-tuning method is developed based on the photovoltaic system and its high performance requirements. The principal task of the tuning method is to adjust the parameters of the Fuzzy Logic (FL) in order to minimize the square of the error between actual and reference output. Simulations with practical parameters show that our proposed MPPT using RNF outperform the conventional MPPT controller terms of tracking speed and accuracy.