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Intelligent Data Mining Approach for Fault Diagnosis

Authors: Yann-Chang Huang; Huo-Ching Sun; Yu-Hsun Lin;

Intelligent Data Mining Approach for Fault Diagnosis

Abstract

This paper presents wavelet analysis and statistical techniques for assessing the insulation condition of power cables. A specific fault is made and placed on the terminal joint of a 25 kV power cable, and the deterioration phenomena is accelerated by the overvoltage method. The deterioration phenomena of the internal insulation material are explained by wavelet analysis and statistical techniques using partial discharge (PD) current pulse waveforms. The PD value reaches its maximum level, and average discharge level rises, before insulation breakdown. However, the discharge numbers and the equivalent time-length of partial discharge current pulse waveforms fall, causing a current pulse with a large amplitude, and a short time period in the final stage of PD. The proposed method is demonstrated to be effective and feasible.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
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