An Efficient Framework Based on Semi-Supervised Learning for Transformer Fault Diagnosis

Transformer fault diagnosis is very important for the stable operation of transformers, but it is still difficult to recognize transformer faults. The main obstacle is attributed to the lack of labeled data for transformer faults. Due to the limited data of transformer faults, a great deal of unlabeled data in real life is not difficult to obtain and contains a lot of effective information. Semi-supervised self-training can significantly enhance the classification accuracy and performance of the base classifier by applying unlabeled data. However, one of the reasons for the degradation of semi-supervised self-training is the lack of initial labeled data to train the initial classifier. In this paper, we propose an improved semi-supervised SMOTE (SS-SMOTE), which uses the potential information contained in unlabeled data to generate synthetic samples, and then reasonably increases the labeled data set. Meanwhile, we combine SS-SMOTE with self-training into an algorithm framework. Finally, a series of experiments are carried out to evaluate the performance of the proposed algorithm, and compared with other existing state-of-the-art semi-supervised algorithms. The results show that the performance improvement of the classifier using the semi-supervised improved SMOTE algorithm framework is effective, and has a good effect on solving the problem of small sample classification. (c) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.