基于支持向量机和交叉验证的变压器故障诊断  

Transformer Fault Diagnosis Based on C-SVC and Cross-validation Algorithm

作　　者：张艳[1] 吴玲[1] 

机构地区：[1]自贡电业局调度局,四川自贡643000

出　　处：《中国电力》2012年第11期52-55,共4页Electric Power

摘　　要：为及时监测变压器潜伏性故障和准确诊断故障,提出基于优化惩罚因子C参数的支持向量机算法(C-SVC:C-support vector classification)和交叉验证算法相结合的变压器故障诊断方法。该方法利用变压器在故障时产生的氢气、甲烷、乙烷、乙烯、乙炔的体积分数数据建立训练集和测试集。在训练集中,该方法能自动优化出(寻找最佳)支持向量机的核函数的参数γ和惩罚因子C,利用优化的参数对训练集进行训练,可得到最佳的支持向量机模型,并用该模型对测试集进行分类,从而诊断出变压器的故障类型。变压器故障诊断实例分析结果证明,该方法可行,有效,且具有较高的故障诊断准确率。A novel method for power transformer fault diagnosis based on the C-SVC(support vector classification with the optimized penalty parameter C) and cross-validation algorithm is presented,which can monitor and detect latent transformer faults timely and accurately.The training and testing sets of the C-SVC algorithm are built upon the data about the dissolved gases including hydrogen,methyl hydride,ethane,aethylenum and acetylene produced from transformer faults.Through the optimizing process of the penalty parameter and kernel function parameter γ in the training set,the optimal support vector machine model can be gotten,with which the classification of data in the testing set can be conducted to determine fault features.The method has been validated by many practical examples to be feasible and efficient with high fault diagnosis accuracy.

关 键 词：变压器 故障诊断 支持向量机 C-SVC算法 交叉验证 核函数参数 

分 类 号：TM407[电气工程—电器] TP18[自动化与计算机技术—控制理论与控制工程]