DocumentCode :
3592542
Title :
Automatic detection-localization of fault point on waveform and classification of power quality disturbance waveshape fault using wavelet and neural network
Author :
Dilokratanatrakool, C. ; Ayudhya, Piyasawat Navaratana Na ; Chayavanich, Tasanee ; Prapanavarat, Cherdchai
Author_Institution :
Dept. of Electr. Eng., King Mongkut´´s Univ. of Technol., Bangkok, Thailand
Volume :
1
fYear :
2003
Firstpage :
142
Abstract :
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.
Keywords :
decision making; fault location; learning (artificial intelligence); neural nets; power engineering computing; power supply quality; power system faults; vector quantisation; wavelet transforms; automatic detection; disturbance waveshape fault; learning vector quantization networks; localization algorithm; multiresolution signal decomposition techniques; neural network; power quality disturbance; voting decision making; wavelet transform analysis; Artificial neural networks; Decision making; Fault detection; Neural networks; Power quality; Signal resolution; Vector quantization; Voting; Wavelet analysis; Wavelet transforms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7925-X
Type :
conf
DOI :
10.1109/RISSP.2003.1285564
Filename :
1285564
Link To Document :
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