In order to improve the accuracy and efficiency of fault diagnosis for transformers and properly evaluate the corresponding statuses,relevance vector machine(RVM)is used to classify the overheating and discharge faults of transformers at first,then adaptive neural-fuzzy inference system(ANFIS)is utilized to identify the faults further,and estimate the probability of fault type.Experimental results show that the proposed method has a strong ability of learning and extracting features;especially,the method with fuzzy set and membership degree can output the probability of fault type in the case of overlapping fault features,which provides decision support for the status evaluation;the accuracy of the RVM-ANFIS method can reach as high as 96.15%,together with a higher calculation efficiency;compared with methods such as support vector machine(SVM)and artificial neural network(ANN),the proposed method has a better efficiency and a higher accuracy.%为了提高变压器故障诊断的准确率和效率,合理评估变压器的状态,本文采用相关向量机RVM先对变压器的过热和放电故障进行划分.用自适应神经模糊推理系统进一步对故障进行分类,并对故障隶属概率进行估计.实验结果表明:本文方法有很强的学习能力和特征提取能力;尤其对于存在重叠区的故障特征,用模糊集和隶属度的方法能够输出故障类型概率,对状态评估进行决策辅助;诊断率高达96.15%,且运算效率高;跟支持向量机、人工神经网络方法相比,有更好的效率和更高的准确率.
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