13 October 2008 Fault diagnosis model of DGA for power transformer based on FCM and SVM
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Support vector machine (SVM) is a novel machine learning based on statistical learning theory, SVM is powerful for the problem with small sample, nonlinear and high dimension. A model of transformer diagnosis based on SVM is present in this paper in which it uses the grid search method based on cross-validation to determine model parameters. Taking into account the compactness characteristics of DGA data, the fuzzy C-means (FCM) clustering method is adopted to pre-select samples achieved. It solves the problem of long time expended on model parameters determined, and enhances a certain promotion of the model extension ability. Practical analysis shows that this model has a good classification results and extension ability.
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Huiqin Sun, Huiqin Sun, Lihua Sun, Lihua Sun, Qingrui Liu, Qingrui Liu, Suzhi Wang, Suzhi Wang, Kejun Sun, Kejun Sun, } "Fault diagnosis model of DGA for power transformer based on FCM and SVM", Proc. SPIE 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence, 71271N (13 October 2008); doi: 10.1117/12.806361; https://doi.org/10.1117/12.806361


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