Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier
Fault Diagnosis of Power Transformer Based on Large Margin Learning Classifier
复制标题
基于大裕量学习分类器的电力变压器故障诊断
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
Jianbing Huo
中科院分区：
文献类型：
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作者：
Xizhao Wang;Mingzhu Lu;Jianbing Huo
电力变压器的故障诊断对于装置的安全和电力系统的可靠性具有重要意义。本文提出了基于SVM超平面大边缘学习理论的大边缘学习分类器，该分类器针对多类问题进行了精心设计。每次它都会尝试找到具有最大余量的分离超平面来分裂簇。大余量学习分类器作为一种新颖的工具，被应用于电力变压器的故障诊断中。由于其非凡的泛化能力，它在可靠性和训练速度上都有出色的表现。实验结果表明了该方法的可行性和有效性
The fault diagnosis of power transformer is important for safety of the device and reliability of the power system. This paper proposes the large margin learning classifier, which is well designed for multi-class problem based on the large margin learning of SVM hyper-planes theory. Each time it attempts to find the separating hyper-plane with maximum margin to split the clusters. As a novel tool, the large margin learning classifier is applied into the fault diagnosis of power transformer. Due to its extraordinary generalization capability, it has excellent performance on reliability and training speed. The experimental results show the feasibility and effectiveness of this method