基于图像处理和半监督学习的变电设备故障诊断

Fault Diagnosis of Substation Equipment Based on Image Processing and Semi-Supervised Learning

变电设备是电力系统中的关键部分,维护其安全稳定运行具有重要意义。当变电设备发生故障时,需要及时、准确对其故障类型进行诊断。提出一种基于图像处理和半监督学习的变电设备故障类型诊断方法。对收集到的红外图像数据进行特征提取,将其中的温度特征、纹理特征和形状特征作为模型的参考向量;利用SMOTE算法,对有标签样本的少数类样本进行样本扩充;汇总样本数据,构建图半监督学习网络,并对其进行训练。相比于传统的有监督学习方法,该文提出的方法能够学习无标签样本数据中的信息。使用真实的样本数据进行测试,验证所提方法的有效性,实验结果表明利用特征提取、样本扩充以及半监督学习模型能够提高变电设备故障的分类准确度。

As a key part of the power system,substation equipment is of great significance to maintain its safe and stable operation. When the substation equipment fails,the fault type needs to be diagnosed timely and accurately. Aiming at this problem,this paper proposes a fault diagnosis method for substation equipment based on image processing and semisupervised learning. First,we execute feature extraction process on the collected infrared image data, and extract the temperature features,texture features and shape features as the model reference vectors. Then, the synthetic minority oversampling technique(SMOTE)algorithm is used to expand the sample of the minority samples with the label. Finally,we aggregate the unlabeled sample data to construct a graph semisupervised learning network and then train this graph. Compared with traditional supervised learning methods, the proposed method in this paper can learn information from unlabeled sample data. Finally,we test our proposed method on real dataset. The experimental results show that the use of feature extraction,sample generation and semi-supervised learning model can improve the accuracy of substation equipment fault classification,which verifies the effectiveness of the method proposed in this paper.