Discrimination among Winding Mechanical Defects in Transformer Using Noise Detection and Data Mining Boosting Method

This paper proposes, an efficient method to detect and discriminate mechanical defects of transformer winding based on extracting the winding frequency responses using outlier data detection and ensemble algorithms, which together constitutes an efficient hybrid method. First, the frequency response of the high voltage winding of a real transformer model (1.6 MVA) was extracted in different condition and arranged as primary data. Then, due to the high standard deviation of the characteristics and the weight of the outlier samples above the threshold of 1.1, the Local Outlier Factor (LOF) method was used to clean the samples. Finally, data mining algorithms have been used to detect and distinguish mechanical defects. Based on the results, the decision tree bagging ensemble method reported the best accuracy compared to other techniques and improved the accuracy of the decision tree with total accuracy of 92.68% by LOF. These results also showed that all methods improved accuracy by LOF. It can, therefore, be claimed that the proposed method is capable of discriminating transformer winding mechanical defects accurately.