Maximum Power Tracking under Complex Partial Shading Conditions in Grid Connected Solar Photovoltaic System

A combination of Cuckoo Search algorithm and Recurrent Neural Network is proposed in this paper to track the maximum power from solar photovoltaic system under complex partial shading conditions with minimum tracking speed. Cuckoo Search is a meta-heuristic search algorithm which imitates the behavior of the cuckoo bird. The Recurrent Neural Network is a highly successful technique in nonlinear systems. It can interpolate and extrapolate the random data with high accuracy if the network is properly trained. There are two stages in the proposed hybrid technique. In stage 1 (Off-line stage), Cuckoo Search algorithm identifies the maximum power under different solar radiation patterns and converts them into control pulses. The control pulses are given as target and the solar radiation of individual photovoltaic module as input to train the Recurrent Neural Network, also the weights are optimized. In stage 2 (On-line stage), the trained Recurrent Neural Network is tested for real time conditions. The proposed hybrid algorithm is implemented in MATLAB/Simulink platform and the tracking performance is compared with the state-of-art methods.