journal6 ›› 2013, Vol. 34 ›› Issue (1): 51-55.DOI: 10.3969/j.issn.1007-2985.2013.01.013

• 物理与电气工程 • 上一篇    下一篇

基于遗传小波神经网络的变压器故障诊断

  

  1. (黑龙江科技学院,黑龙江 哈尔滨 150027)
  • 出版日期:2013-01-25 发布日期:2013-01-22
  • 通讯作者: 王雪丹(1956-),女,黑龙江哈尔滨人,黑龙江科技学院教授,硕士生导师,博士,主要从事交直流电力拖动系统与控制、谐波抑制和无功功率补偿等研究;E-mail:wxd06@163.com.
  • 作者简介:马桂雨(1987-),男,山西大同人,黑龙江科技学院硕士研究生,主要从事人工智能在电力系统自动化中的应用研究

Power Transformer Fault Diagnosis Based on Genetic Wavelet Neural Network

  1. (Heilongjiang University of Science and Technology,Harbin 150027,China)
  • Online:2013-01-25 Published:2013-01-22

摘要:电力变压器油中溶解气体的色谱分析是变压器故障诊断的重要方法,通过该方法可以间接了解变压器的运行状态和内部潜在故障.人工神经网络已经成功地应用于电力变压器故障诊断,但学习样本数多和输入输出关系复杂性减慢了网络的收敛速度.为解决此问题,将用遗传算法改进的小波神经网络应用于电力变压器故障诊断,克服小波算法易于陷入局部极小、收敛速度慢等缺点.

关键词: 小波神经网络, 遗传算法, 变压器故障诊断

Abstract: The chromatographic analysis of the power transformer oil dissolved gas is an important method for transformer fault diagnosis by which the operating state of the transformer and the potential transformer internal fault can be grasped indirectly.Artificial neural network has been applied in the power transformer fault diagnosis successfully,but the  large number of learning samples and the complicated input-output relationship will lead to a slow network convergence.To resolve the problem,this paper employ the wavelet neural network improved by using genetic algorithms  in power transformer fault diagnosis,thus overcoming the shortcomings of  local minima and slow convergence speed.

Key words: wavelet neural network, genetic algorithms, power transformer fault diagnosis

公众号 电子书橱 超星期刊 手机浏览 在线QQ