Hybrid MPPT Controller for Photovoltaic System under Partial Shading Condition

This paper introduces a hybrid maximum power point tracking controller for grid connected PV system under partial shading condition (PSC). The proposed technique uses the capabilities of Artificial Neural Network (ANN) to predict the maximum voltage under different shading Condition and the brain inspired Cerebellum Controller for controlling the converter based on the output of ANN algorithm. A comprehensive study is made with traditional maximum power point tracking method such as Perturb and Observe(P&O), and ANN with Proportional-Integral-Derivative (PID) Controller by examining the parameters such as Maximum Power Point Tracking (MPP) efficiency, ripple in the PV Power, settling time and Percentage of Total Harmonic Distortion. The Cerebellum controller overcomes the limitations of PID by incorporating more sophisticated learning mechanism inspired by the human cerebellum for providing improved performance in handling nonlinear systems. The suggested method is implemented in MATLAB/SIMULINK, the results indicate that compared to conventional MPPT controllers the proposed method exhibits higher maximum power point (MPP) tracking efficiency of 99.4-99.5% under shading condition.