Synthesized diagnosis on transformer fault based on principle component analysis and support vector machine

A synthesized method is presented in this paper for transformer fault diagnosis, This model combines the principal component analysis and the support vector machine. Firstly, by principal component analysis, the characteristics of the sample data are extracted, the main information is be retrieved, a new sample set is created. Then, a support vector machine model is created and the new sample set is used to train the support vector machines. This method achieves the advantages of the two algorithms. The accuracy of transformer fault diagnosis based on this method is improved when the sample information is noisy or incomplete. Experimental results show that the method is valid and feasible and has better diagnostic accuracy.