全球能源互联网（英文）2021，Vol.4Issue(6)：596-607,12.DOI:10.14171/j.2096-5117.gei.2021.06.007
基于红外图像处理和半监督学习的变压器故障诊断方法
Fault diagnosis of electric transformers based on infrared image processing and semi-supervised learning
摘要
Abstract
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.关键词
变压器/故障诊断/红外图像/生成对抗网络/半监督学习Key words
Transformer/Fault diagnosis/Infrared image/Generative adversarial network/Semi-supervised learning引用本文复制引用
方健,杨帆,童锐,覃煜,代晓丰..基于红外图像处理和半监督学习的变压器故障诊断方法[J].全球能源互联网（英文）,2021,4(6):596-607,12.基金项目
This work was supported by China Southern Power Grid Co.Ltd.science and technology project(Research on the theory,technology and application of stereoscopic disaster defense for power distribution network in large city,GZHKJXM20180060)and National Natural Science Foundation of China(No.51477100). （Research on the theory,technology and application of stereoscopic disaster defense for power distribution network in large city,GZHKJXM20180060）
