配电变压器是电力系统的重要组成部分,其安全稳定运行至关重要.如果变压器发生故障,必须及时准确地诊断故障类型.为此,论文提出了一种基于红外图像处理和半监督学习的变压器故障诊断方法.首先,对采集的红外图像数据进行特征提取,提取温度、纹理和形状特征作为模型参考向量.然后,构造生成对抗网络(GAN),与传统的监督学习方法不同,该网络可以从未标记的样本数据中学习信息,并以标记样本为基础,通过少数子集生成合成样本.随后,在整个数据集上训练半监督模型,即标记和未标记数据,进而.最后,我们根据电力实际数据集上对论文方法进行了测试.实验结果表明,采用特征提取、样本生成和半监督学习模型可以提高变压器故障分类的准确性,验证了论文方法具有有效性与实用性.“,”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.