C213 Automatic Detection-Localization of Fault Point on Waveform and Classification of Power Quality Disturbance Waveshape Fault Using Wavelet and Neural Network

Dilokratanatrakool Choosak; Navaratana Na Ayudhya Piyasawat; Chayavanich Tasanee; Prapanavarat Cherdchai

doi:10.1299/jsmeicope.2003.2._2-241_

2003.2

DOI https://doi.org/10.1299/jsmeicope.2003.2._2-241_

会議情報

C213 Automatic Detection-Localization of Fault Point on Waveform and Classification of Power Quality Disturbance Waveshape Fault Using Wavelet and Neural Network

Dilokratanatrakool Choosak, Navaratana Na Ayudhya Piyasawat, Chayavanich Tasanee, Prapanavarat Cherdchai

著者情報

キーワード: Artificial neural networks, learning vector quantization network, multiresolution signal decomposition, voting decision making scheme, wavelet transform analysisy, waveshape fault disturbance signal

会議録・要旨集フリー

p. _2-241_-_2-246_

詳細

抄録

In this paper a new method for automatically detecting, localizing and classifying various types of disturbance waveshape fault is presented. The method is based on wavelet transform analysis, artificial neural networks, and the mathematical theory of evidence. The proposed detection and localization algorithm is carried out in the wavelet transform using multiresolution signal decomposition techniques. The proposed classification is carried out in sets of multiple neural network using a learning vector quantization networks. The outcomes of the networks are then integrated using voting decision making scheme.

責任著者(Corresponding author)

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