A photovoltaic maximum power tracking using neural networks

The paper presents laboratory implementation of a photovoltaic artificial neural network (ANN) based maximum power tracking controller. The control objective is to track the maximum available solar power in a photovoltaic array interfaced to an electric utility grid via a line-commutated inverter. The inverse dynamic characteristics of this interface scheme is identified via off-line training using a multi-layer perceptron type neural network. The ANN output is used as the control signal to vary the line-commutated inverter firing control angle, hence track the available maximum solar power. The weights of the ANN are also updated by a novel on-line training algorithm which utilizes the on-line power mismatch error. This ensures on-line maximum solar power tracking. The proposed controller is compared with a well tuned conventional proportional plus integral controller to validate its effectiveness