Wind Energy Conversion Systems and Artificial Neural Networks: Role and Applications

Jaber Alshehri, Ahmed Alzahrani, Muhammad Khalid

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

The deployment of renewable energy sources along with the conventional power network have raised many challenges that require the implementation of modern and intelligent solutions. Wind energy conversion systems (WECSs) have transformed significantly since artificial neural networks (ANNs), intensively emerged into their applications. This paper presents a relatively comprehensive review of the role and applications of ANNs in wind energy conversion systems. The ANNs definition, types of activation functions, major application areas and a detailed overview of the WECSs are provided. The main part of the paper addresses in detail the applications of ANNs in WECS. Particularly, three main applications are discussed, which are wind speed prediction, wind power control, and diagnosis identifications. Finally, the last section highlights some of the main findings and provides a comparison between different artificial neural networks algorithms in terms of popularity in WECS applications.

Original languageEnglish
Title of host publication2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1777-1782
Number of pages6
ISBN (Electronic)9781728135205
DOIs
StatePublished - May 2019

Publication series

Name2019 IEEE PES Innovative Smart Grid Technologies Asia, ISGT 2019

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Artificial neural network
  • and wind power control
  • diagnosis identification
  • wind energy conversion systems
  • wind speed prediction

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Control and Optimization

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