Pattern recognition applications for power system disturbance classification

A. M. Gaouda; S. H. Kanoun; M. M.A. Salama; A. Y. Chikhani

doi:10.1109/TPWRD.2002.1022786

Pattern recognition applications for power system disturbance classification

A. M. Gaouda, S. H. Kanoun, M. M.A. Salama, A. Y. Chikhani

Department of Electrical and Communication Engineering

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

103 Citations (Scopus)

Abstract

This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.

Original language	English
Pages (from-to)	677-683
Number of pages	7
Journal	IEEE Transactions on Power Delivery
Volume	17
Issue number	3
DOIs	https://doi.org/10.1109/TPWRD.2002.1022786
Publication status	Published - Jul 2002

Keywords

K-nearest neighbor
Minimum Euclidean distance
Muitiresolution signal decomposition
Neural network recognition techniques
Power quality
Wavelet analysis

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/TPWRD.2002.1022786

Cite this

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title = "Pattern recognition applications for power system disturbance classification",

abstract = "This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.",

keywords = "K-nearest neighbor, Minimum Euclidean distance, Muitiresolution signal decomposition, Neural network recognition techniques, Power quality, Wavelet analysis",

author = "Gaouda, {A. M.} and Kanoun, {S. H.} and Salama, {M. M.A.} and Chikhani, {A. Y.}",

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AU - Gaouda, A. M.

AU - Kanoun, S. H.

AU - Salama, M. M.A.

AU - Chikhani, A. Y.

PY - 2002/7

Y1 - 2002/7

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AB - This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.

KW - K-nearest neighbor

KW - Minimum Euclidean distance

KW - Muitiresolution signal decomposition

KW - Neural network recognition techniques

KW - Power quality

KW - Wavelet analysis

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JO - IEEE Transactions on Power Delivery

JF - IEEE Transactions on Power Delivery

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ER -

Pattern recognition applications for power system disturbance classification

Abstract

Keywords

ASJC Scopus subject areas

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