Data Augmentation Method for Transformer Fault Based on Improved Auto-Encoder Under the Condition of Insufficient Data

There are few transformer faults, which makes the methods of transformer fault diagnosis based on machine learning lack of data. For this reason, a method based on improved auto-encoder (IAE) is proposed to augment transformer fault data. Firstly, to solve the problem of limited data and lack of diversity in the traditional automatic encoder, an improved strategy for generating samples for transformer faults is proposed. Secondly, considering that the traditional convolutional neural network will lose a lot of feature information in the pooling operation, the improved convolutional neural network (ICNN) is constructed as the classifier of fault diagnosis. Finally, the effectiveness and adaptability of the proposed method are verified by the actual data. The simulation results show that IAE can take into account the distribution and diversity of data at the same time, and the generated transformer fault data can improve the performance of the classifier better than the traditional augmentation methods such random over-sampling method, synthetic minority over-sampling technique, and auto-encoder. Compared with traditional classifiers, ICNN has higher fault diagnosis accuracy before and after data augmentation. © 2021, Electrical Technology Press Co. Ltd. All right reserved.