Detection of Inrush Current Using S-Transform and Probabilistic Neural Network

被引：0
|
作者：
Mokryani, G. [1 ,3 ]
Haghifam, M. -R. [2 ]
Latafat, H. [1 ]
Aliparast, P. [1 ,3 ]
Abdollahy, A. [1 ]
机构：
[1] Islamic Azad Univ, Soofian Branch, Tehran, Iran
[2] Tarbiat Modares Univ, Tehran, Iran
[3] Islamic Azad Univ, Youth Res Club, Tehran, Iran
来源：
2010 IEEE PES TRANSMISSION AND DISTRIBUTION CONFERENCE AND EXPOSITION: SMART SOLUTIONS FOR A CHANGING WORLD | 2010年
关键词：
Probabilistic Neural Network (PNN); inrush current; EMTP program; S-transform; MAGNETIZING INRUSH; IDENTIFICATION; DISCRIMINATION; COMPUTATION; FAULTS; MODEL;
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：
Transformer inrush currents are high magnitude, harmonic-rich currents generated when transformer cores are driven into saturation during energization. This paper presents an S-Transform based Probabilistic Neural Network (PNN) classifier for recognition of inrush current. Using this method inrush current can be discriminate from other transients such as capacitor switching, load switching and single phase to ground fault. S-transform is used for feature extraction and PNN is used for classification. Inrush current data and other transients are obtained by simulation using EMTP program. The simulation results reveal that the combination of S-Transform and PNN can effectively detect inrush current from other events.
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收藏
页数：6
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