High-impedance fault detection utilizing a Morlet wavelet transform approach

Shyh Jier Huang; Cheng Tao Hsieh

doi:10.1109/61.796234

High-impedance fault detection utilizing a Morlet wavelet transform approach

Shyh Jier Huang, Cheng Tao Hsieh

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

156 Citations (Scopus)

Abstract

An application of Morlet wavelets to the analysis of high-impedance fault generated signals is proposed in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. The proposed method has been applied to discriminate the high-impedance faults from the normal switching events, and to examine the faults under various grounds including Portland cement, wet soil and grass. Testing results have demonstrated the practicality and advantages of the proposed method for the applications.

Original language	English
Pages (from-to)	1401-1407
Number of pages	7
Journal	IEEE Transactions on Power Delivery
Volume	14
Issue number	4
DOIs	https://doi.org/10.1109/61.796234
Publication status	Published - 1999

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/61.796234

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abstract = "An application of Morlet wavelets to the analysis of high-impedance fault generated signals is proposed in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. The proposed method has been applied to discriminate the high-impedance faults from the normal switching events, and to examine the faults under various grounds including Portland cement, wet soil and grass. Testing results have demonstrated the practicality and advantages of the proposed method for the applications.",

author = "Huang, {Shyh Jier} and Hsieh, {Cheng Tao}",

note = "Funding Information: The authors would like to express their thanks to thc financial support given by the National Scicncc Council of the Republic of China uiider contract number NSC87-22 13-E-006-088 and the technical support from Taiwan Powcr Company.",

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N2 - An application of Morlet wavelets to the analysis of high-impedance fault generated signals is proposed in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. The proposed method has been applied to discriminate the high-impedance faults from the normal switching events, and to examine the faults under various grounds including Portland cement, wet soil and grass. Testing results have demonstrated the practicality and advantages of the proposed method for the applications.

AB - An application of Morlet wavelets to the analysis of high-impedance fault generated signals is proposed in this paper. With the time-frequency localization characteristics embedded in wavelets, the time and frequency information of a waveform can be presented as a visualized scheme. Different from the fast Fourier transform, the wavelet transform approach is more efficient in monitoring fault signals as time varies. The proposed method has been applied to discriminate the high-impedance faults from the normal switching events, and to examine the faults under various grounds including Portland cement, wet soil and grass. Testing results have demonstrated the practicality and advantages of the proposed method for the applications.

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High-impedance fault detection utilizing a Morlet wavelet transform approach

Abstract

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