NDLI: Wind generator tower vibration fault diagnosis and monitoring based on PCA

Content Provider	IEEE Xplore Digital Library
Author	Ning Fang Peng Guo
Copyright Year	2013
Description	Author affiliation: Sch. Of Control & Comput. Eng., North China Electr. Power Univ., Beijing, China (Ning Fang; Peng Guo)
Abstract	Vibration signal is one kind of important variables for wind turbine in Supervisory Control and Data Acquisition (SCADA) System. With modeling and analysis of vibration signals, incipient failure of key components such as tower, drive train and rotor could be detected. In this paper, Principal Component Analysis (PCA) was applied to model the tower vibration providing a good understanding of the tower vibration dynamic characteristics and the main factors influencing these. The merits and physical interpretation of PCA are also discussed and analyzed. With SCADA of one wind turbine during April to June in 2006, PCA model for tower vibration in normal working condition was established and validated. This method monitoring the operation of the wind generator by calculating the monitoring statistic Hotelling $T^{2}$ $(T^{2}$ for short) and SPE. If $T^{2}$ and SPE statistics over confidence limit, the decision system is abnormal. We can calculate contribution rate of each variable on the main component,then get the cause of the fault . Wind generator fault data set simulation verify the effectiveness of the proposed method.
Sponsorship	IEEE Control Syst. Soc.
Starting Page	1924
Ending Page	1929
File Size	265490
Page Count	6
File Format	PDF
ISBN	9781467355339
e-ISBN	9781467355346
DOI	10.1109/CCDC.2013.6561248
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vibrations Condition monitoring Wind speed Poles and towers Tower vibration Data models Wind turbines Modeling Principal Component Analysis Monitoring Principal component analysis
Content Type	Text
Resource Type	Article

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