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Application of Data Mining Technology Based on FRS and SVM for Fault Identification of Power Transformer

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3 Author(s)
Zhihong Xue ; Dept. of Electr. & Inf., Hebei Univ. of Sci. & Technol., Shijiazhuang, China ; Xiaoyun Sun ; Yongchun Liang

Data mining (DM) technology based on fuzzy rough set (FRS) and support vector machine (SVM) are presented to classify the fault of power transformer. Improper or inadequate dissolved gases analysis (DGA) data may lead to failure fault classification of power transformer. SVM, through statistical learning theory, provides a way of classification information by generating optimal kernel based representative DGA data. In order to make full use of the classification ability of SVM and improve the fault classification accuracy, FRS is used to pre-classify the transformer fault and the multi-level power transformer fault diagnosis model based on FRS and SVM was presented in this paper. By comparing with the traditional method like neural network, there is less fault data discriminated by FRS and SVM model and the accuracy for power transformer fault diagnosis is improved.

Published in:

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on  (Volume:2 )

Date of Conference:

7-8 Nov. 2009

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