Transformer Winding Fault Diagnosis Using Vibration Image and Deep Learning
Kaixing Hong, Ming Jin, Huang Hai
Abstract
Winding condition assessment is an essential task for operating transformers, and the vibration method provides a low-cost and non-intrusive approach. In this paper, a novel feature extraction method based on vibration analysis is proposed, which converts the vibration monitoring data with load information into a vibration image. Then, a deep learning approach based on convolutional neural network (CNN) is used to classify the images belong to different classes. In the laboratory experiment, free vibration tests are performed on an on-load winding model, which are used to verify the relationship between the natural frequency and the electromagnetic force under different clamping forces. During the field experiment, transformers are divided into three categories, including normal, degraded and anomalous, and the proposed scheme is trained and tested by using the vibration samples acquired from more than 100 operating transformers. The performance of the CNN classifier under different input sizes is investigated, which achieves 98.3% overall accuracy. Besides, the confusion matrices obtained by other methods are compared, such as artificial neural network (ANN), support vector machine (SVM) and naive Bayes classifier (NBC). The results show that the proposed scheme including the vibration image extraction method and the CNN classifier offers superior performance in winding fault diagnosis.