组合核相关向量机在电力变压器故障诊断中的应用研究

Study on Application of Multi-kernel Learning Relevance Vector Machines in Fault Diagnosis of Power Transformers

仅依据反映变压器运行状态的单一特征信息很难对变压器的状态做出正确的诊断,而组合核相关向量机可实现多特征空间的融合。鉴于此,提出了基于组合核相关向量机的变压器故障诊断新方法。该诊断方法可融合蕴含变压器运行状态的多种特征信息,输出变压器为各种状态的概率,为变压器的检修提供更多的可用信息。此外,为进一步提高组合核相关向量机的性能,提出了基于K折交叉验证和遗传算法的核函数参数优化方法,对组合核相关向量机进行了优化。实例分析表明,与BP神经网络、支持向量机诊断方法相比,该文所提方法具有较好的故障诊断效果。

It is hard to improve the transformer fault diagnosis accuracy only based on single feature information.While multi-kernel learning relevance vector machine（MKL-RVM） can enable informative integration of possibly heterogeneous sources.A new power transformer fault diagnosis method based on MKL-RVM was proposed.The method integrated the feature information of power transformer operating state,and outputted the probabilities of various power transformer operating states,giving more available information for the maintenance and repair of the power transformer.Additionally,in order to enhance the performance of the MKL-RVM,genetic algorithm combined with K-cross validation was adopted to optimize the kernel function parameters.Experimental results showed that the proposed method was capable of more excellent diagnosis accuracy to back propagation neural network（BPNN） and support vector machine（SVM）.