【摘要】 针对变压器故障数据不均衡导致变压器故障诊断精度不高的问题，提出一种基于Borderline-SMOTEIHT混合采样的改进GWO-SVM变压器故障诊断方法。首先，利用Borderline-SMOTE算法选择最具代表性的边界样本生成少数类新样本，利用IHT算法剔除多数类中的噪声样本或边缘样本，增大类间特征的差异性。其次，基于差分进化思想，在灰狼算法中引入动态收敛因子和概率突变机制，对SVM模型中的惩罚因子和核参数进行优化，以提高算法的全局搜索能力和收敛精度。最后，通过实验对比分析，证明了所提方法的有效性。更多还原

【Abstract】 A modified GWO-SVM transformer fault diagnosis method based on Borderline-SMOTE-IHT mixed sampling is proposed to address the issue of low accuracy in transformer fault diagnosis caused by imbalanced transformer fault data. Firstly,the Borderline SMOTE algorithm is used to select the most representative boundary samples to generate new minority class samples,and the IHT algorithm is used to remove noise or edge samples from the majority class,increasing the differences in features between classes.Secondly,based on the idea of differential evolution,the dynamic convergence factor and probability mutation mechanism are introduced into the gray wolf algorithm to optimize the penalty factor and kernel parameters in the SVM model for improving the algorithm’s global search ability and convergence accuracy. Finally,the effectiveness of the proposed method is demonstrated through experimental comparative analysis.更多还原