Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller

摘要: The photovoltaic (PV) generator exhibits a nonlinear V-I characteristic and its maximum power (MP) point varies with solar insolation. In this paper, feedforward MP-point tracking scheme is developed for the coupled-inductor interleaved-boost-converter-fed PV system using fuzzy controller. proposed converter has lower switch current stress improved efficiency over noncoupled system. For given insolation, algorithm changes duty ratio of such that cell array voltage equals corresponding to MP point. This done by loop, which generates an error signal comparing instantaneous reference Depending on change signals, controller control pulsewidth-modulation in turn adjusts converter. loop obtained offline trained neural network. Experimental data are used training network, employs backpropagation algorithm. peak effectiveness demonstrated through simulation experimental results. Tracking performance also compared conventional proportional-plus-integral-controller-based These studies reveal results better performance.