NDLI: Features of vibration signal of power transformer using local wave method

Content Provider	IEEE Xplore Digital Library
Author	Zhong-Li Wu Yong-Li Zhu
Copyright Year	2009
Description	Author affiliation: School of Control Science and Engineering, North China Electric Power University, Baoding071003, China (Zhong-Li Wu; Yong-Li Zhu)
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.
Starting Page	388
Ending Page	393
File Size	226617
Page Count	6
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212556
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power transformers Vibrations Transformer cores Condition monitoring Signal processing Magnetic cores Time frequency analysis Signal analysis Magnetostriction Machine learning Fault diagnosis Power transformer Vibration Local wave method
Content Type	Text
Resource Type	Article

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