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.