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
收藏
页码：677 / 683
页数：7
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