Power quality disturbance waveform recognition using wavelet-based neural classifier - Part 1: Theoretical foundation

被引：201

作者：

Santoso, S ^{[1
]}

Powers, EJ ^{[1
]}

Grady, WM ^{[1
]}

Parsons, AC ^{[1
]}

机构：

[1] Univ Texas, Dept Elect & Comp Engn, Austin, TX 78712 USA

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2000年 / 15卷 / 01期

关键词：

artificial neural networks; Dempster-Shafer theory of evidence; pattern recognition; power quality; voting scheme; wavelet transforms;

D O I：

10.1109/61.847255

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：

Existing techniques for recognizing and identifying power quality disturbance waveforms are primarily based on visual inspection of the waveform, It is the purpose of this paper to bring to bear recent advances, especially in wavelet transforms, artificial neural networks, and the mathematical theory of evidence, to the problem of automatic power quality disturbance waveform recognition. Unlike past attempts to automatically identify disturbance waveforms where the identification is performed in the time domain using an individual artificial neural network, the proposed recognition scheme is carried out in the wavelet domain using a set of multiple neural networks. The outcomes of the networks are then integrated using decision making schemes such as a simple voting scheme or the Dempster-Shafer theory of evidence. With such a configuration, the classifier is capable of providing a degree of belief for the identified disturbance waveform.

引用

页码：222 / 228

页数：7