Wavelet aided SVM Classifier for Stator Inter-Turn Fault Monitoring in Induction Motors

被引：0

作者：

Das, S. ^{[1
]}

Koley, C. ^{[2
]}

Purkait, P. ^{[1
]}

Chakravorti, S. ^{[3
]}

机构：

[1] HIT, Dept Elect Engn, Haldia 721657, WB, India

[2] Natl Inst Technol, Dept Elect Engn, Durgapur, India

[3] Jadavpur Univ, Dept Elect Engn, Kolkata 700032, India

来源：

IEEE POWER AND ENERGY SOCIETY GENERAL MEETING 2010 | 2010年

关键词：

Induction motors; stator turn fault; Concordia pattern; continuous wavelet transform; Park's transformation; support vector machine; SHORT CIRCUITS; IDENTIFICATION; DIAGNOSIS; SYSTEM; WINDINGS; FAILURE; TIME;

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：

Early detection of faults in stator winding is crucial for reliable and economical operation of induction motors in industries. Whereas major winding faults can be easily identified from supply current magnitude, minor faults involving less than 5% of turns are not readily discernible. The present work documents experimental results for monitoring of minor short circuit faults in stator windings of induction motor. Motor line current has been analyzed using modern signal processing and data reduction tools combining Park's transformation and Continuous Wavelet Transform (CWT). Support Vector Machine (SVM) based data classification tool has been used for fault characterization based on fault features extracted using CWT.

引用

页数：6

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