Title :
Implementation of an artificial neural network based controller for a photovoltaic energy scheme
Author :
Mashaly, Hussein M. ; Sharaf, Adel M. ; Mansour, Mohamed M. ; El-Sattar, Abmed A.
Author_Institution :
Dept. of Electr. Eng., New Brunswick Univ., Fredericton, NB, Canada
fDate :
27 Jun-2 Jul 1994
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
Keywords :
intelligent control; invertors; multilayer perceptrons; neurocontrollers; photovoltaic power systems; real-time systems; tracking; line-commutated inverter; maximum solar power tracking; multilayer perceptron; neural network based controller; online power mismatch error; online training algorithm; photovoltaic array; photovoltaic energy scheme; tracking controller; Artificial neural networks; Inverters; Laboratories; Multi-layer neural network; Multilayer perceptrons; Neural networks; Photovoltaic systems; Power industry; Solar energy; Solar power generation;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374621
