Classification of power quality problems using wavelet based artificial neural network

被引：0
|
作者：
Chandel, A. K. [1 ]
Guleria, G. [2 ]
Chandel, R. [3 ]
机构：
[1] Natl Inst Technol, Dept Elect Engn, Hamirpur 177005, HP, India
[2] NIT Hamirpur, Hamirpur, HP, India
[3] Natl Inst Technol, Dept Elect Commun Engn, Hamirpur, HP 177005, India
来源：
2008 IEEE/PES TRANSMISSION & DISTRIBUTION CONFERENCE & EXPOSITION, VOLS 1-3 | 2008年
关键词：
power quality; classification; wavelet; neural network;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
In this paper, a wavelet based artificial neural network classifier for recognizing power quality disturbances is implemented and tested. Discrete wavelet transforms based multi-resolution signal decomposition technique is integrated with the feed-forward neural network model to develop the power quality problem classifier. Classification of the power quality problems has been carried out in two parts. In first part, multi-resolution signal decomposition analysis with Parseval's energy theorem is used to extract the energy features of the power quality signal. In the second part, this feature information is used to develop neural network classifier. The classifier has been tested on various disturbances viz. voltage sag, swell, momentary interruption, capacitor switching and single fine to ground fault. Results obtained show the versatility of the classifier for classifying the most commonly power quality problems.
引用
收藏
页码：689 / +
页数：2
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