Pattern recognition applications for power system disturbance classification

被引：74

作者：

Gaouda, AM ^{[1
]}

Kanoun, SH

Salama, MMA

Chikhani, AY

机构：

[1] Univ Waterloo, Dept Elect & Comp Engn, Waterloo, ON N2L 3G1, Canada

[2] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada

[3] Royal Mil Coll Canada, Dept Elect & Comp Engn, Kingston, ON K7K 7B4, Canada

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2002年 / 17卷 / 03期

关键词：

k-nearest neighbor; minimum Euclidean distance; multiresolution signal decomposition; neural network recognition techniques; power quality; wavelet analysis;

D O I：

10.1109/TPWRD.2002.1022786

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：677 / 683

页数：7

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