Implementation of an artificial neural network based controller for a photovoltaic energy scheme

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 multilayer 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 online training algorithm which utilizes the online power mismatch error. This ensures online maximum solar power tracking. >