Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application

Author

Santoso, Surya ; Powers, Edward J. ; Grady, W. Mack ; Parsons, Antony C.

Author_Institution

Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA

Volume

15

Issue

1

fYear

2000

fDate

1/1/2000 12:00:00 AM

Firstpage

229

Lastpage

235

Abstract

For pt.I see ibid., vol.15, no.1, p.222-8 (2000). A wavelet-based neural classifier is constructed and thoroughly tested under various conditions, The classifier is able to provide a degree of belief for the identified waveform. The degree of belief gives an indication about the goodness of the decision made. It is also equipped with an acceptance threshold so that it can reject ambiguous disturbance waveforms. The classifier is able to achieve the accuracy rate of more than 90% by rejecting less than 10% of the waveforms as ambiguous

Keywords

belief maintenance; inference mechanisms; neural nets; pattern recognition; power supply quality; power system analysis computing; power system faults; waveform analysis; Dempster-Shafer theory of evidence; acceptance threshold; ambiguous disturbance waveforms rejection; degree of belief; power quality disturbance waveform recognition; wavelet-based neural classifier; Capacitors; Decision making; Frequency; Helium; Neural networks; Power quality; Power system reliability; Testing; Voting; Wavelet domain;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.847256

Filename

847256