DocumentCode
3098457
Title
Features of vibration signal of power transformer using local wave method
Author
Wu, Zhong-li ; Zhu, Yong-li
Author_Institution
Sch. of Control Sci. & Eng., North China Electr. Power Univ., Baoding, China
Volume
1
fYear
2009
fDate
12-15 July 2009
Firstpage
388
Lastpage
393
Abstract
The mechanical faults of a transformer are predicted with monitoring its vibration, which is a new technology put forward recently for condition based monitoring of transformers. The key technology of vibration method is how to identify the effective features of the vibration signal. Here, a new self-adapting time-frequency analysis method - local wave method which is developed from the empirical mode decomposition is applied, and it is combined with the Hilbert transform to get the time-frequency distribution of local wave, which can qualitatively describe the relationship between time and frequency, and achieves a effective recognition of the non-linear, non-stationary signals. Experimental results in this paper demonstrate that the features of the vibration signal can be effectively found out with local wave method, and the energy distribution of the vibration signal to frequency bands can obtained. The mechanical status of the core and winding of a transformer can be effectively diagnosed by the frequency bands-energy distribution. The local wave method is a effective method for diagnosing the mechanical faults and the severity of the faults of power transformers.
Keywords
Hilbert transforms; fault diagnosis; power transformers; signal processing; vibrations; Hilbert transform; local wave method; mechanical faults; nonlinear signal; nonstationary signals; power transformer; self-adapting time-frequency analysis; vibration signal; Condition monitoring; Machine learning; Magnetic cores; Magnetostriction; Power transformers; Signal analysis; Signal processing; Time frequency analysis; Transformer cores; Vibrations; Condition monitoring; Fault diagnosis; Local wave method; Power transformer; Vibration;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2009 International Conference on
Conference_Location
Baoding
Print_ISBN
978-1-4244-3702-3
Electronic_ISBN
978-1-4244-3703-0
Type
conf
DOI
10.1109/ICMLC.2009.5212556
Filename
5212556
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