Abstract
In this study we explore the feasibility of applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to the prediction of incipient power transformer faults. A clonal selection algorithm (CSA) is introduced for the first time in the literature to select optimal input features and RBF kernel parameters. CSA is shown to be capable of improving the speed and accuracy of classification systems by removing redundant and potentially confusing input features, and of optimizing the kernel parameters simultaneously. Simulation results on practice data demonstrate the effectiveness and high efficiency of the proposed approach.
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Lee, TF., Cho, MY., Shieh, CS., Lee, HJ., Fang, FM. (2006). Diagnosis of Incipient Fault of Power Transformers Using SVM with Clonal Selection Algorithms Optimization. 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_65
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DOI: https://doi.org/10.1007/11875604_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
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