Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning

It is crucial to maintain the safe and stable operation of distribution transformers, which constitute a key part of power systems. In the event of transformer failure, the fault type must be diagnosed in a timely and accurate manner. To this end, a transformer fault diagnosis method based on infrared image processing and semi-supervised learning is proposed herein. First, we perform feature extraction on the collected infrared-image data to extract temperature, texture, and shape features as the model reference vectors. Then, a generative adversarial network (GAN) is constructed to generate synthetic samples for the minority subset of labelled samples. The proposed method can learn information from unlabeled sample data, unlike conventional supervised learning methods. Subsequently, a semi-supervised graph model is trained on the entire dataset, i.e., both labeled and unlabeled data. Finally, we test the proposed model on an actual dataset collected from a Chinese electricity provider. The experimental results show that the use of feature extraction, sample generation, and semi-supervised learning model can improve the accuracy of transformer fault classification. This verifies the effectiveness of the proposed method.