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文章摘要
基于粒子群K均值算法的变压器在线故障诊断研究
The Research on the On-line Fault Diagnosis of Transformer Based on PSO and Kmeans Algorithm
Received:November 12, 2014  Revised:November 23, 2014
DOI:
中文关键词: 粒子群算法  K均值算法  油色谱  故障诊断  改良三比值法
英文关键词: PSO  Kmeans algorithm  Oil chromatogram  Fault diagnosis  Improved three-ration method
基金项目:国家973计划资助项目(2011CB707904); 国家自然科学基金(61070078)
Author NameAffiliationE-mail
Yanxiaohu* State Grid Electric Power Research Institute yanxiaohupaul@126.com 
He Fazhi Computer School, Wuhan University  
Lu Wen-hua State Grid Electric Power Research Institute  
GU Kaikai State Grid Electric Power Research Institute  
XIANG Dongdong State Grid Electric Power Research Institute  
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中文摘要:
      本文提出了基于粒子群K均值算法的变压器在线故障诊断方法。首先通过K均值算法得到代表各种变压器故障类型的三比值,然后利用粒子群算法进行优化。当改良的三比值法由于缺码不能进行分析时,计算待判样本的三比值到各类故障对应三比值的距离,选择距离最小的三比值对应的故障类型为该样本的故障。然后结合在线监测数据,利用专家规则库融合多参量对故障进行综合诊断。最后通过实例验证了本文方法的可行性和有效性。
英文摘要:
      The on-line fault diagnosis of transformer based on PSO and kmeans algorithm is proposed in the paper. Firstly, the three rations data of oil chromatogram is clustered by the kmeans algorithm. Then the three rations that can represent every type of the transformer fault are optimized using PSO. When the diagnosis cannot be analyzed by the improved three-ration method caused of code deficiency, the distance between the three rations of the oil chromatographic sample that need to be analyzed and the optimal three rations of every fault type is computed. The fault type with the nearest distance is the final fault type of the sample. Secondly, combined with the multi-parameter on-line monitor data, the transformers fault is diagnosed with rule library. Finally, the feasibility and efficiency of the method proposed in the paper is demonstrated by the experiment.
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