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Modeling Analysis of Power Transformer Fault Diagnosis Based on Improved Relevance Vector Machine

机译：基于改进的关联向量机的电力变压器故障诊断建模分析。

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摘要

A new method of transformer fault diagnosis based on relevance vector machine (RVM) is proposed. Bayesian estimation is applied to support vector machine (SVM) in the novel algorithm, which made fault diagnosis system work more effectively. In the paper, the analysis model is presented that the solutions of RVM have the feature of sparsity and RVM can obtain global solutions under finite samples. The process of transformer fault diagnosis for four working statuses is given in experiments and simulations. The results validated that this method has obvious advantages of diagnosis time and accuracy compared with backpropagation (BP) neural networks and general SVM methods.
机译：提出了一种基于相关向量机的变压器故障诊断方法。该算法将贝叶斯估计应用于支持向量机，使得故障诊断系统更有效地工作。提出了RVM解具有稀疏性的分析模型，RVM可以在有限样本下获得全局解。实验和仿真给出了四种工作状态下变压器故障诊断的过程。结果证明，与反向传播（BP）神经网络和常规SVM方法相比，该方法具有明显的诊断时间和准确性。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第14期|636374.1-636374.6|共6页
  • 作者

    Lutao Liu; Zujun Ding;

  • 作者单位

    College of Information and Telecommunication, Harbin Engineering University, Harbin 150001, China;

    Department of Electronic and Electrical Engineering, Huaiyin Institute of Technology, Huai'an 223001, China;

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