Recognition of Multiple PQ Disturbances Using Wavelet-based Neural Networks - Part 2: Implementation and Applications

Author

Cheng-Long Chuang ; Yen-Ling Lu ; Tsong-Liang Huang ; Ying-Tung Hsiao ; Joe-Air Jiang

Author_Institution

Graduate Inst. of Bio-Ind. Mechatronics Eng., Nat. Taiwan Univ., Taipei

fYear

2005

fDate

18-18 Aug. 2005

Firstpage

1

Lastpage

6

Abstract

For part I see ibid., p.Z001593-8 (2005). This work proposes and implements a novel classifier integrated with the wavelet transform and dynamic structural neural network for recognizing multiple power quality disturbances in a measured waveform. The classifier has been tested under different PQ events such as with single disturbance, dual disturbances and multiple disturbances. The experimental results show that the proposed classifier can achieve high accuracy rate more than 97% under various test cases

Keywords

neural nets; pattern classification; power engineering computing; power supply quality; power system faults; power system measurement; wavelet transforms; dynamic structural neural network; multiple power quality disturbances; pattern classifier; pattern recognition; wavelet transform; Amplitude estimation; Artificial neural networks; Feature extraction; Graphical user interfaces; Neural networks; Power quality; Testing; Voltage fluctuations; Wavelet analysis; Wavelet transforms; Power quality; neural networks; pattern recognition; wavelet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES

Conference_Location

Dalian

Print_ISBN

0-7803-9114-4

Type

conf

DOI

10.1109/TDC.2005.1546957

Filename

1546957