Classification of Power Quality Disturbances Using S-Transform Based Artificial Neural Networks

被引：6

作者：

Kaewarsa, Suriya ^{[1
]}

机构：

[1] Rajamangala Univ Technol Isan, Fac Ind & Technol, Dept Elect Engn, Sakon Nakhon, Thailand

来源：

2009 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND INTELLIGENT SYSTEMS, PROCEEDINGS, VOL 1 | 2009年

关键词：

power quality disturbance; S-transform; artificial neural network; wavelet transform; RECOGNITION; SPECTRUM;

D O I：

10.1109/ICICISYS.2009.5357780

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a method based on S-transform and artificial neural network for detection and classification of power quality disturbances The input features of the neural network are extracted using S-transform The features obtained from the S-transform are distinct, understandable and immune to noise These features after normalization are given to a feed forward neural network trained by the back propagation algorithm The data required to develop the network are generated by simulating various faults in a test system The proposed method requires less number of features and less memory space without losing its original property The simulation results show that the proposed method is effective and can classify the power quality signals even under noisy environment

引用

页码：566 / 570

页数：5

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