Multi-class Support Vector Machine approach for fault classification in power transmission line

This paper presents an approach for the fault classification in transmission line using multi-class support vector machine (SVM). This approach uses information obtained from the wavelet decomposition of post fault current signals as input to SVM for classification of various faults that may occur i...

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Bibliographic Details
Main Authors: Malathi, V., Marimuthu, N.S.
Format: Conference Proceeding
Language:English
Subjects:
Fault classification
multi-class support vector machine
Pattern recognition
Power system protection
Power transmission lines
Protective relaying
Radial basis function networks
Relays
Statistical learning
support vector machine
Support vector machine classification
Support vector machines
transmission line
Transmission line theory
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Summary:This paper presents an approach for the fault classification in transmission line using multi-class support vector machine (SVM). This approach uses information obtained from the wavelet decomposition of post fault current signals as input to SVM for classification of various faults that may occur in transmission line. Using MATLAB Simulink, dataset has been generated with different types of fault and system variables, which include fault resistance, fault distance and fault inception angle. The proposed method has been extensively tested on a 240-kV, 200-km transmission line under variety of fault conditions. The results indicate that the proposed technique is accurate and robust for a variation in system parameter and fault conditions.
ISSN:2165-4387
DOI:10.1109/ICSET.2008.4746974