Fault diagnosis of oil-immersed power transformers using common vector approach

This paper considers the problem of classifying power transformer faults in the incipient stage by using dissolved gas analysis (DGA) data. To solve this problem with high accuracy, we propose to use the common vector approach (CVA) that is a successful classifier when the number of data is insufficient. The feature vector required for the training and testing phases of the CVA is established by using both raw dissolved gas analysis data and some characteristics extracted from this data. The performance of the proposed method is evaluated over DGA data sets supplied from the Turkish Electricity Transmission Company and is compared with some conventional and intelligent methods in terms of classification accuracy and training/testing duration. The achieved results show that the proposed method exhibits superior performance than that of the other methods compared in the meaning of both diagnosis accuracy and computational time. Analysis performed on the physical faults, where the transformers fault types are verified with the electrical test methods, confirms the validity and reliability of the proposed method, as well. Being free from parameter settings is another advantage of this method for using it in online oil-gas analysis applications.