Feature vector extraction for the automatic classification of power quality disturbances

被引：0
|
作者：
Lee, CH
Lee, JS
Kim, JO
Nam, SW
机构：
来源：
ISCAS '97 - PROCEEDINGS OF 1997 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS I - IV: CIRCUITS AND SYSTEMS IN THE INFORMATION AGE | 1997年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The objective of this paper is to present a systematic approach to feature vector extraction for the automatic classification of power quality (PQ) disturbances, where discrete wavelet transform (DWT), signal power estimation and data compression methods are utilized to improve the classification performance and reduce computational complexity. To demonstrate the performance and applicability of the proposed method, some test results obtained by analyzing 7-class power quality disturbances, generated by the EMTP, with white Gaussian noise are also provided.
引用
收藏
页码：2681 / 2684
页数：4
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