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。
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.
作者
张爱兰
许志元
杨琦欣
刘春明
朱彦玮
李晓磊
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)
出处
《电工电气》
2019年第6期15-20,共6页
Electrotechnics Electric
关键词
变压器故障
多级诊断
改进D-S证据理论
信息融合
transformer fault
multi-level diagnosis
improved D-S evidence theory
information fusion
