Design of Power Transformer Fault Diagnosis Model Based on Support Vector Machine

Tao Liu, Zhijie Wang
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引用次数: 9

Abstract

Support Vector Machines (SVM) is a machine-learning algorithm based on statistical learning theory. The method for power transformer fault diagnosis based on SVM is proposed in this paper. The principle and algorithm of this method are introduced. Through a finite learning sample the relation is established between the transformer fault signature and the quantity of its dissolved gas. A faults classifier is constructed by using the dissolved gas data of the fault transformer. The testing results show that this method can successfully be applied to the diagnosis of gear faults.
基于支持向量机的电力变压器故障诊断模型设计
支持向量机(SVM)是一种基于统计学习理论的机器学习算法。提出了一种基于支持向量机的电力变压器故障诊断方法。介绍了该方法的原理和算法。通过有限学习样本,建立了变压器故障特征与其溶解气体量之间的关系。利用故障变压器的溶解气体数据构造了故障分类器。试验结果表明,该方法可以成功地应用于齿轮故障的诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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