基于代价敏感组合核相关向量机的电力变压器故障诊断

摘要

组合核相关向量机可以融合多个特征空间,输出变压器隶属于各种状态的概率.将代价敏感机制融入组合核相关向量机,构建了代价敏感组合核相关向量机,该算法以误诊断代价最小为目标,按贝叶斯风险理论预测样本的故障类别,克服了传统诊断方法未考虑误诊断代价差异的问题.针对代价敏感组合核相关向量机核函数参数选取需人为设定的问题,采用K折交叉验证和粒子群算法相结合的方法寻优核函数参数.基于油中溶解气体分析(dissolved gas analysis, DGA)数据的诊断实例表明,与BP神经网络,支持向量机及组合核相关向量机算法相比,代价敏感组合核相关向量机不仅具有较高的诊断正确率,而且具有较低的误诊断代价.%Multi-Kernel learning relevance vector machine can integrate multiple feature spaces, and output the probability belonging to each state.In this paper, cost-sensitive mechanism was integrated into the multi-Kernel learning relevance vector machine (MKL-RVM), and constructed the cost-sensitive multi-Kernel learning relevance vector machine.The algorithm is based on Bayesian risk theory to predict the fault category of samples, reaching the goal of minimum cost of misdiagnosis, and overcomes the problem of not taking account of the difference cost of misdiagnosis.To solve the problem of its kernel function parameters need to be set artificially, K-fold cross validation combined particle swarm optimization was adapted to optimize the kernel function parameters.Case analysis based on dissolved gas analysis (DGA) data shows that CS-MKL-RVM not only has higher diagnosis accuracy, but also has lowest misdiagnosis cost, when compared with BP neural network, support vector machine and multi-Kernel learning relevance vector machine.