中国电力 ›› 2015, Vol. 48 ›› Issue (5): 41-45.DOI: 10.11930.2015.5.41

• 安全专栏 • 上一篇    下一篇

应用贝叶斯框架的LS-SVM概率输出诊断电力变压器故障

秦鹏,赵峰   

  1. 兰州交通大学 电气工程与自动化学院,甘肃 兰州 730070
  • 收稿日期:2014-12-28 出版日期:2015-05-25 发布日期:2015-11-27
  • 作者简介:秦鹏(1988—),男,甘肃金昌人,硕士研究生,从事电力变压器故障诊断方面的研究。E-mail: qinpeng198811052@163.com
  • 基金资助:
    甘肃省自然科学基金资助项目(1310RJZA038)

Fault Diagnosis Method for Power Transformer Based on LS-SVM Probability Output of Bayesian Framework

QIN Peng, ZHAO Feng   

  1. School of Automation and Electrical Engineering Lanzhou Jiaotong University, Lanzhou 730070, China
  • Received:2014-12-28 Online:2015-05-25 Published:2015-11-27
  • Supported by:
    This work is supported by the Natural Science Foundation of Gansu Province, China (No. 1310RJZA038).

摘要: 针对传统最小二乘支持向量机(LS-SVM)分类器的参数选择具有随意性和不确定性等不足,采用贝叶斯推断方法、通过3级分层推断优化来确定最小二乘支持向量机的各参数,有效提高了最小二乘支持向量机的建模效率。结合最小二乘支持向量机的后验概率输出,可将其运用到变压器故障诊断中。仿真结果表明:该方法能有效地诊断电力变压器故障,且诊断精度和建模效率均优于传统的最小二乘支持向量机方法。

关键词: 变压器, 故障诊断, 最小二乘支持向量机, 参数选择, 建模效率, 诊断精度, 贝叶斯推断, 概率输出

Abstract: In order to remedy the randomness and uncertainty in selection process, the parameters of the least squares support vector machines (LS-SVM) classifier are optimally selected by the Bayesian inference with three levels hierarchy which can significantly improves modeling efficiency. Combined with probability outputs of multiclass LS-SVMS, the Bayesian inference LS-SVM classification method is applied to diagnose the power transformer fault diagnosis. The experimental simulation results show that the proposed approach can identify faults successfully. Both the diagnosis accuracy and modeling efficiency are better than traditional LS-SVM method.

Key words: power transformer, fault diagnosis, least squares support vector machines, parameters, modeling, diagnosis accuracy, bayesian inference, probability outputs

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