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.
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Lee, TF., Cho, MY., Shieh, CS., Lee, HJ., Fang, FM. (2006). Particle Swarm Optimization-Based SVM for Incipient Fault Classification of Power Transformers. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_11
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DOI: https://doi.org/10.1007/11875604_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
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