摘要
基于油中溶解气体(dissolved gas analysis,DGA)构造的故障征兆作为变压器故障诊断的重要先验信息,其质量直接影响诊断效果。目前,基于DGA气体的故障征兆数量繁多,但种类相对单一且诊断效果有限,为实现更高准确率的变压器故障诊断,该文提出云征兆方法以丰富现有比值征兆集。为适应高维云征兆的云变换,设计自组织云概念提取神经网络(self-organized cloud concept extraction,SOCCE)进行云概念的提取,以深度挖掘多DGA气体间的关联信息,提高智能算法的诊断能力。最后,通过先排序后寻优的征兆优选策略遴选出最优的DGA混合征兆集。通过IEC TC10故障数据库下的对比诊断可知,该文优选的混合新征兆能够实现92.4%的诊断准确率,相较于传统征兆诊断准确率提升了13.2%~30.8%,且在现场应用和多诊断模型中均表现出较强的泛化能力和推广能力。
The fault features based on dissolved gas analysis(DGA)is the important prior information for transformer fault diagnosis,and the quality of features directly affect the diagnostic effect.At present,there are many fault features based on DGA gas,whereas,the types are relatively single and the diagnostic effect is limited.To realize transformer fault diagnosis with higher accuracy,the cloud feature method is proposed to enrich the existing ratio feature set.To adapt the cloud transformation of high-dimensional cloud feature,the neural network of self-organized cloud concept extraction(SOCCE)is proposed to extract cloud concepts,so as to deeply mine the associated information between multi DGA gases and improve the diagnostic capability of intelligent algorithm.Finally,the optimal DGA hybrid feature set is selected through the feature optimization strategy in which ranking is followed by optimizing.Through the comparative diagnosis based on the IEC TC10 database,it can be seen that the optimal hybrid new features can achieve the fault diagnosis accuracy of 92.4%,which is an improvement of 13.2%~30.8%compared with the diagnosis accuracy of the traditional features.In addition,the new features show the strong ability in field application and multi diagnostic models.
作者
白星振
臧元
葛磊蛟
李长云
李晶
原希尧
BAI Xingzhen;ZANG Yuan;GE Leijiao;LI Changyun;LI Jing;YUAN Xiyao(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;Key Laboratory of Smart Grid of Ministry of Education,Tianjin University,Tianjin 300072,China;College of Electronic and Information Engineering,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2023年第9期3864-3875,共12页
High Voltage Engineering
基金
国家自然科学基金(61803233)。
关键词
变压器
故障诊断
油中溶解气体
云变换
特征优选
transformer
fault diagnosis
gas dissolved in oil
cloud transformation
feature selection
