Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network
Zhenhua Li, ZhenHua Li, Yujie Zhang, Ahmed Abu‐Siada, Xingxin Chen, Zhenxing Li, Zhenxing Li, Yanchun Xu, Lei Zhang, Yue Tong
Abstract
While frequency response analysis (FRA) is a well matured technique widely used by current industry practice to detect the mechanical integrity of power transformers, interpretation of FRA signatures is still challenging, regardless of the research efforts in this area. This paper presents a method for reliable quantitative and qualitative analysis to the transformer FRA signatures based on a decision tree classification model and a fully connected neural network. Several levels of different six fault types are obtained using a lumped parameter-based transformer model. Results show that the proposed model performs well in the training and the validation stages, and is of good generalization ability.