Applications of wavelet transform and fuzzy neural network on power quality recognition

Chiung Chou Liao, Hong-Tzer Yang, Ying Chun Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The wavelet transform coefficients (WTCs) contain plenty of information needed for transient event identification of power quality (PQ) events. However, adopting WTCs directly has the drawbacks of taking a longer time and too much memory for the recognition system. To solve the abovementioned recognition problems and to effectively reduce the number of features representing power transients, spectrum energies of WTCs in different scales are calculated by Parseval's Theorem. Through the proposed approach, features of the original power signals can be reserved and not influenced by occurring points of PQ events. The fuzzy neural classification systems are then used for signal recognition and fuzzy rule construction. Success rates of recognizing PQ events from noise-riding signals are proven to be feasible in power system applications in this paper.

Original languageEnglish
Title of host publicationInternational Conference on Power Control and Optimization
Subtitle of host publicationInnovation in Power Control for Optimal Industry
Pages19-24
Number of pages6
DOIs
Publication statusPublished - 2008 Nov 12
EventInternational Conference on Power Control and Optimization, PCO 2008 - Chiang Mai, Thailand
Duration: 2008 Jul 182008 Aug 20

Publication series

NameAIP Conference Proceedings
Volume1052
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

OtherInternational Conference on Power Control and Optimization, PCO 2008
CountryThailand
CityChiang Mai
Period08-07-1808-08-20

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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