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Mathematical Problems in Engineering
Volume 2012, Article ID 194091, 12 pages
Research Article

Application of ENN-1 for Fault Diagnosis of Wind Power Systems

Department of Electrical Engineering, National Chin-Yi University of Technology, Number 35, Lane 215, Section 1, Chung-Shan Road, Taichung, Taiping 411, Taiwan

Received 20 March 2012; Accepted 20 May 2012

Academic Editor: Jitao Sun

Copyright © 2012 Meng-Hui Wang and Hung-Cheng Chen. 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.


Maintaining a wind turbine and ensuring secure is not easy because of long-term exposure to the environment and high installation locations. Wind turbines need fully functional condition-monitoring and fault diagnosis systems that prevent accidents and reduce maintenance costs. This paper presents a simulator design for fault diagnosis of wind power systems and further proposes some fault diagnosis technologies such as signal analysis, feature selecting, and diagnosis methods. First, this paper uses a wind power simulator to produce fault conditions and features from the monitoring sensors. Then an extension neural network type-1- (ENN-1-) based method is proposed to develop the core of the fault diagnosis system. The proposed system will benefit the development of real fault diagnosis systems with testing models that demonstrate satisfactory results.