Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines

Ravikumar, B and Thukaram, Dhadbanjan and Khincha, HP (2007) Intelligent Approach for Fault Diagnosis in Power Transmission Systems Using Support Vector Machines. In: International Journal of Emerging Electric Power Systems, 8 (4). pp. 1-32.

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Abstract

This paper presents an approach for identifying the faulted line section and fault location on transmission systems using support vector machines (SVMs) for diagnosis/post-fault analysis purpose. Power system disturbances are often caused by faults on transmission lines. When fault occurs on a transmission system, the protective relay detects the fault and initiates the tripping operation, which isolates the affected part from the rest of the power system. Based on the fault section identified, rapid and corrective restoration procedures can thus be taken to minimize the power interruption and limit the impact of outage on the system. The approach is particularly important for post-fault diagnosis of any mal-operation of relays following a disturbance in the neighboring line connected to the same substation. This may help in improving the fault monitoring/diagnosis process, thus assuring secure operation of the power systems. In this paper we compare SVMs with radial basis function neural networks (RBFNN) in data sets corresponding to different faults on a transmission system. Classification and regression accuracy is reported for both strategies. Studies on a practical 24-Bus equivalent EHV transmission system of the Indian Southern region is presented for indicating the improved generalization with the large margin classifiers in enhancing the efficacy of the chosen model.

Item Type:	Journal Article
Publication:	International Journal of Emerging Electric Power Systems
Publisher:	Walter de Gruyter GmbH & Co. KG.
Additional Information:	Copyright of this article belongs to Walter de Gruyter GmbH & Co. KG.
Keywords:	fault diagnosis;support vector machines;radial basis function neural networks;substation automation
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	10 Apr 2012 08:25
Last Modified:	11 Apr 2012 07:58
URI:	http://eprints.iisc.ac.in/id/eprint/44234

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