AI Approach for FRA Based Condition Assessment of Power Transformers

Condition assessment of power transformers are important to avoid power system failures because it usually cause significant economic losses. In order to detect the mechanical integrity of power transformer windings, Frequency response measurements (FRA) has been developed. It is an effective tool to detect the internal mechanical integrity. However, interpretation of FRA results can sometimes be tedious when comparing transformers with different structures. This paper presents a FRA measurements based AI approach to recognize different faulty and non-faulty transformers irrespectively of their design structures. FRA data sets were collected from 10, Field aged power transformers with different capacities and each data set consisted of 100 features i.e., 4 FRA frequency bands, 5 FRA measuring methods and 5 statistical analyzing parameters. It was found that with two Principal Components, faulty and non faulty transformers could be easily separated. Accordingly, the 100 features were reduced to 64 features by optimizing FRA measuring methods from 5 to 3. Then different ML techniques and NN algorithms were used to further classify the faulty transformers into three different fault types. It was found that fine kNN was the best method for transformer classification with a 100% accuracy of the tested samples.