A Fault Classification Method in Power Systems Using DWT and SVM Classifier

被引：0

作者：

Livani, Hanif ^{[1
]}

Evrenosoglu, Cansin Yaman ^{[1
]}

机构：

[1] Virginia Tech, Dept Elect & Comp Engn, Blacksburg, VA 24061 USA

来源：

2012 IEEE PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION (T&D) | 2012年

关键词：

Discrete wavelet transform; fault classification; SVM; traveling waves; SUPPORT VECTOR MACHINES; NEURAL-NETWORK;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

This paper presents a method for fault classification in the power systems using a combination of support vector machine (SVM) classifier and Wavelet Transformation. Measurements from only one bus are utilized. Discrete Wavelet Transform (DWT) is used to extract the transient information of recorded voltages. The normalized wavelet energy of post-fault voltage and normalized energy of the post-fault currents are used as the input to the classifier. The classifier is trained with different fault scenarios in the power system. The transient voltages and phase currents for different types of faults and locations along the power system are obtained through ATP simulations. MATLAB is used to process the simulated transient voltages and apply the proposed method. The performance of the method is evaluated for two different networks; an overhead line combined with an underground cable and a 6-bus distribution network.

引用

页数：5

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