A quantitative comparison of wavelet based feature vectors for classification of power quality disturbances.

被引：0
|
作者：
Dash, PK [1 ]
Lee, IWC [1 ]
Basu, KP [1 ]
Morris, S [1 ]
Sharaf, AM [1 ]
机构：
[1] Silicon Inst Technol, Bhubaneswar, Orissa, India
来源：
IECON'03: THE 29TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1 - 3, PROCEEDINGS | 2003年
关键词：
wavelet transform; S-Transform; power quality;
D O I：
10.1109/IECON.2003.1280023
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a comparison between different wavelet feature vectors for power quality disturbance classification problems. Three different wavelet algorithms are simulated and applied on nine classes of power quality disturbances. Neural networks are then used to compute the classification accuracy of the feature vectors. Certain characteristics of the wavelet feature vectors are apparent from the results.
引用
收藏
页码：454 / 459
页数：6
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