Artificial neural networks for controlling wind-PV power systems: A review


Karabacak K., Cetin N. S.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, vol.29, pp.804-827, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Review
  • Volume: 29
  • Publication Date: 2014
  • Doi Number: 10.1016/j.rser.2013.08.070
  • Title of Journal : RENEWABLE & SUSTAINABLE ENERGY REVIEWS
  • Page Numbers: pp.804-827
  • Keywords: Artificial neural networks, Renewable energy, Photovoltaic, Wind energy, Control systems, Hybrid power systems, CURRENT-VOLTAGE CURVE, SOLAR-RADIATION DATA, ENERGY, TURBINE, MODEL, GENERATION, SPEED, TRANSLATION, SIMULATION, SEQUENCES

Abstract

Nowadays, renewable energy systems are taking place than the conventional energy systems. Especially, PV systems and wind energy conversion systems (WECS) are taking a big role in supplying world's energy necessity. Efficiency of such types of renewable energy systems is being tried to be improved by using different methods. Besides conventional methods, intelligent system designs are seem to be more useful to improve efficiency of renewable energy systems. However, artificial neural networks (ANN) have many usage areas in modeling, simulation and control of renewable energy systems. ANNs are easy to use and to implement renewable energy system designs. In this paper, artificial neural network applications of PV, WECS and hybrid renewable energy systems which consist of PV and WECS are presented. Usage of neural network structures in such types of systems have been motivated. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.