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Computational Intelligence and Neuroscience
Volume 2014, Article ID 580972, 13 pages
http://dx.doi.org/10.1155/2014/580972
Research Article

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran

Received 1 October 2013; Accepted 3 February 2014; Published 11 March 2014

Academic Editor: Christian W. Dawson

Copyright © 2014 Nasser Talebi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Martin Bach-Andersen, Bo Rømer-Odgaard, and Ole Winther, “Flexible non-linear predictive models for large-scale wind turbine diagnostics,” Wind Energy, 2016. View at Publisher · View at Google Scholar
  • M. Bourogaoui, H. Ben Attia Sethom, and I. Slama Belkhodja, “Speed/position sensor fault tolerant control in adjustable speed drives – A review,” ISA Transactions, 2016. View at Publisher · View at Google Scholar
  • Mehmet Şimşir, Raif Bayır, and Yılmaz Uyaroğlu, “Real-Time Monitoring and Fault Diagnosis of a Low Power Hub Motor Using Feedforward Neural Network,” Computational Intelligence and Neuroscience, vol. 2016, pp. 1–13, 2016. View at Publisher · View at Google Scholar
  • Yi Chai, Qiu Tang, Hao Ren, Jian-Feng Qu, and Xin Ye, “Deep learning for fault diagnosis: The state of the art and challenge,” Kongzhi yu Juece/Control and Decision, vol. 32, no. 8, pp. 1345–1358, 2017. View at Publisher · View at Google Scholar
  • Alberto Pliego Marugán, Fausto Pedro García Márquez, Jesus María Pinar Perez, and Diego Ruiz-Hernández, “A survey of artificial neural network in wind energy systems,” Applied Energy, vol. 228, pp. 1822–1836, 2018. View at Publisher · View at Google Scholar