RMS percent of wavelet transform for the detection of stochastic high impedance faults

T. M. Lai; Wai Chau Edward Lo; W. M. To; K. H. Lam

doi:10.1109/ICHQP.2012.6381245

RMS percent of wavelet transform for the detection of stochastic high impedance faults

T. M. Lai, Wai Chau Edward Lo, W. M. To, K. H. Lam

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

14 Citations (Scopus)

Abstract

High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. This paper presents a practical pattern recognition based algorithm for electric distribution high impedance fault detection. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with HIF. The analysis using rms ratios of Discrete Wavelet Transform (DWT) yields three phase voltage and current in the low frequency range which are fed to a classifier for pattern recognition. The classifier is based on the algorithm using artificial neural network (ANN) approach. A HIF model was also developed, where the random nature of the arc was simulated using MATLAB.

Original language	English
Title of host publication	Proceedings of 2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012
Pages	823-828
Number of pages	6
DOIs	https://doi.org/10.1109/ICHQP.2012.6381245
Publication status	Published - 1 Dec 2012
Event	2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012 - Hong Kong, Hong Kong Duration: 17 Jun 2012 → 20 Jun 2012

Conference

Conference	2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012
Country/Territory	Hong Kong
City	Hong Kong
Period	17/06/12 → 20/06/12

Keywords

High Impedance Faults
Pattern Recognition
Wavelet Transforms

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing
Energy Engineering and Power Technology
Fuel Technology
Electrical and Electronic Engineering

More information

10.1109/ICHQP.2012.6381245

Cite this

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title = "RMS percent of wavelet transform for the detection of stochastic high impedance faults",

abstract = "High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. This paper presents a practical pattern recognition based algorithm for electric distribution high impedance fault detection. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with HIF. The analysis using rms ratios of Discrete Wavelet Transform (DWT) yields three phase voltage and current in the low frequency range which are fed to a classifier for pattern recognition. The classifier is based on the algorithm using artificial neural network (ANN) approach. A HIF model was also developed, where the random nature of the arc was simulated using MATLAB.",

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Lai, TM, Lo, WCE, To, WM & Lam, KH 2012, RMS percent of wavelet transform for the detection of stochastic high impedance faults. in Proceedings of 2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012., 6381245, pp. 823-828, 2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012, Hong Kong, Hong Kong, 17/06/12. https://doi.org/10.1109/ICHQP.2012.6381245

RMS percent of wavelet transform for the detection of stochastic high impedance faults. / Lai, T. M.; Lo, Wai Chau Edward; To, W. M. et al.
Proceedings of 2012 IEEE 15th International Conference on Harmonics and Quality of Power, ICHQP 2012. 2012. p. 823-828 6381245.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Lai, T. M.

AU - Lo, Wai Chau Edward

AU - To, W. M.

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AB - High impedance faults (HIF) are faults which are difficult to detect by overcurrent protection relays. This paper presents a practical pattern recognition based algorithm for electric distribution high impedance fault detection. The scheme recognizes the distortion of the voltage and current waveforms caused by the arcs usually associated with HIF. The analysis using rms ratios of Discrete Wavelet Transform (DWT) yields three phase voltage and current in the low frequency range which are fed to a classifier for pattern recognition. The classifier is based on the algorithm using artificial neural network (ANN) approach. A HIF model was also developed, where the random nature of the arc was simulated using MATLAB.

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KW - Pattern Recognition

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ER -

RMS percent of wavelet transform for the detection of stochastic high impedance faults

Abstract

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Keywords

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