Fault diagnosis model of DGA for power transformer based on FCM and SVM

Huiqin Sun; Lihua Sun; Qingrui Liu; Suzhi Wang; Kejun Sun

doi:10.1117/12.806361

13 October 2008 Fault diagnosis model of DGA for power transformer based on FCM and SVM

Huiqin Sun, Lihua Sun, Qingrui Liu, Suzhi Wang, Kejun Sun

Proceedings Volume 7127, Seventh International Symposium on Instrumentation and Control Technology: Sensors and Instruments, Computer Simulation, and Artificial Intelligence; 71271N (2008) https://doi.org/10.1117/12.806361
Event: Seventh International Symposium on Instrumentation and Control Technology, 2008, Beijing, China

Abstract

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.

Citation Download Citation

Huiqin Sun, Lihua Sun, Qingrui Liu, Suzhi Wang, and 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); https://doi.org/10.1117/12.806361

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