Abstract: A model and relative solving steps for power transformer fault diagnosis are proposed on the basis of Euclidean Clustering（EC） and Support Vector Machine（SVM） theory in this paper.Some key dissolved gases are selected as the inputs of the diagnosis model and data preprocessing is applied for these gases.According to thirteen fault types，a new model based on EC diagnosis model and SVM models is constructed.By comparing with the other methods，the proposed model reduces the error ratio，and recognition results show that this model is effective.

摘要： 提出一个基于欧氏聚类（Euclidean Clustering，EC）和支持向量机（Support Vector Machine，SVM）的变压器故障诊断模型及其求解步骤。选择典型油中气体作为模型的输入参数，按照变压器常见的13种故障类型，利用训练集样本数据建立基于EC和SVM多分类的组合故障诊断模型。通过与其他组合诊断的方法进行比较证明了该模型的有效性。