| 引用本文: | 顾 民,葛良全.基于自组织神经网络的变压器故障诊断[J].电力系统保护与控制,2007,35(23):28-30,34. |
| GU Min,GE Liang-quan.Fault diagnosis of power transformers based on the neural network of self-organizing map[J].Power System Protection and Control,2007,35(23):28-30,34 |
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| 摘要: |
| 变压器故障诊断实质上是属于一种模式识别，基于类内样本与类中心的距离的不同而对类中心的偏移产生不同影响的思想，改善了传统ART2网络存在模式飘移的不足。首先对变压器DGA故障样本的输入矢量进行扩展处理，然后用改进后的ART2网络对样本进行诊断。通过与传统的三比值法和BP神经网络的诊断结果对比，验证了该方法的有效性和可行性。 |
| 关键词: 电力变压器 故障诊断 油中溶解气体分析 ART2网络 欧氏距离 |
| DOI：10.7667/j.issn.1674-3415.2007.23.007 |
| 分类号: |
| 基金项目:国家自然科学基金项目（40374051） |
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| Fault diagnosis of power transformers based on the neural network of self-organizing map |
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GU Min, GE Liang-quan
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School of Nuclear Technology and Automation ,Chengdu Univercity of Technology, Chengdu 610059,China
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| Abstract: |
| Fault Diagnosis of Power Transformers is a kind of pattern recognition. The paper modifies the traditional ART-2 network based on this idea: different distances for samples to center of cluster produce different influence on excursion of center of cluster and the lack of pattern drifting is improved. First the input vector of sample is extended, then it is diagnosed by the modified ART2-network .The results are compared with those obtained by the IEC three-ratio and BPNN method. Comparison result verify that the algorithm proposed is effective and feasibile. |
| Key words: power transformer fault diagnosis dissolved gas-in-oil analysis ART2 network euclidean distance |
