Suzhou Electric Appliance Research Institute
期刊号: CN32-1800/TM| ISSN1007-3175

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基于信息融合的变压器故障多级诊断方法

来源:电工电气发布时间:2019-06-13 14:13 浏览次数:560
基于信息融合的变压器故障多级诊断方法
 
张爱兰,许志元,杨琦欣,刘春明,朱彦玮,李晓磊
(国网山东省电力公司济南供电公司,山东 济南 250012)
 
    摘 要:建立了基于信息融合的变压器故障多级诊断模型,该模型融合了在线监测、油中溶解气体、电气试验等多源数据信息。采用自适应遗传算法优化的小波神经网络对变压器故障进行初级诊断,通过改进D-S证据理论对初级诊断结果进行决策级融合,实现对变压器故障的深度诊断与定位。通过应用实例证明,该方法可以有效提高变压器故障诊断的精度和可信度,减小诊断的不确定性。
    关键词:变压器故障;多级诊断;改进D-S证据理论;信息融合
    中图分类号:TM411     文献标识码:A     文章编号:1007-3175(2019)06-0015-06
 
Multi-Level Diagnosis Method of Transformer Fault Based on Information Fusion
 
ZHANG Ai-lan, XU Zhi-yuan, YANG Qi-xin, LIU Chun-ming, ZHU Yan-wei, LI Xiao-lei
(State Grid Shandong Electric Power Company Jinan Power Supply Company, Jinan 250012, China)
 
    Abstract: This paper established a multi-level diagnosis model of transformer faults based on information fusion. This model integrated multi-source data information in transformer faults, such as the on-line monitoring data, dissolved gas in oil and electrical test. The adaptive genetic algorithm was adopted to optimize the wavelet neural network, so as to implement primary diagnosis of transformer faults. The improved D-S evidence theory was used to carry out decision-Level fusion of primary diagnostic results to realize the depth diagnosis and location of transformer faults. The application example shows that this method can improve the accuracy and reliability of transformer fault diagnosis, reducing the diagnostic uncertainty.
    Key words: transformer fault; multi-level diagnosis; improved D-S evidence theory; information fusion
 
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