基于混合采样和支持向量机的变压器故障诊断

针对变压器不平衡数据集对变压器故障诊断模型产生的影响，提出了基于混合采样和支持向量机（support vector machines, SVM）的变压器故障诊断方法，利用合成少数类过采样技术（synthetic minority oversampling technique, SMOTE）和基于最近邻规则的欠采样方法，分别对变压器故障数据和正常数据进行采样，再利用混合采样得到的平衡数据训练基于支持向量机变压器故障诊断模型。通过测试集对比不平衡数据和平衡数据下基于SVM的变压器故障诊断模型的性能。最后分析了采样率对于变压器故障诊断模型诊断准确率的影响。实验结果表明，该方法可以有效降低不平衡数据对诊断模型的影响，提高变压器故障诊断模型的准确率。

Transformer Fault Diagnosis Based on Hybrid Sampling and Support Vector Machines

Aiming at the impact of transformer imbalanced data set on transformer fault diagnosis model. A transformer fault diagnosis method based on hybrid sampling and support vector machines (SVM) is proposed. It uses synthetic minority oversampling technique (SMOTE) and under sampling method based on nearest neighbor rules to underestimate transformer fault data and normal data, respectively. Sampling and oversampling, and then using the balanced data obtained by hybrid sampling training based on support vector machines transformer fault diagnosis model. The performance of the SVM-based transformer fault diagnosis model is compared through the test set under imbalanced data and balanced data. Finally, the influence of sampling rate on the diagnostic accuracy of transformer fault diagnosis model is analyzed. Experimental results show that this method can effectively reduce the impact of imbalanced data on the diagnostic model and improve the diagnostic accuracy of the transformer fault diagnostic model.