Abstract
The paper presents laboratory implementation of a photovoltaic artificial neural network (ANN) based maximum power tracking controller. The control purpose 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 scheme is identified by off-line training of a multi-layer perceptron type neural network. The ANN output is used as the control signal to vary the line-commutated inverter firing angle, hence track the available maximum solar power. The weights of the ANN is updated by an on-line training algorithm which utilizes the on-line power mismatch error. This ensures on-line maximum solar power tracking.