Particle swarm optimization-based SVM for incipient fault classification of power transformers

Authors:
Tsair-Fwu Lee;Ming-Yuan Cho;Chin-Shiuh Shieh;Hong-Jen Lee;Fu-Min Fang
Affiliations:
National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan ROC;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan ROC;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan ROC;National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan ROC;Chang Gung Memorial Hospital-Kaohsiung Medical Center, Chang Gung University, College of Medicine, Kaohsiung, Taiwan ROC
Venue:
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Year:
2006

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Abstract

A successful adoption and adaptation of the particle swarm optimization (PSO) algorithm is presented in this paper. It improves the performance of Support Vector Machine (SVM) in the classification of incipient faults of power transformers. A PSO-based encoding technique is developed to improve the accuracy of classification. The proposed scheme is capable of removing misleading input features and, optimizing the kernel parameters at the same time. Experiments on real operational data had demonstrated the effectiveness and efficiency of the proposed approach. The power system industry can benefit from our system in both the accelerated operational speed and the improved accuracy in the classification of incipient faults.