电力系统及其自动化学报2018，Vol.30Issue(3)：35-41,7.DOI:10.3969/j.issn.1003-8930.2018.03.005
RVM和ANFIS用于变压器故障诊断及状态评估
Fault Diagnosis and Status Evaluation for Transformers Using RVM and ANFIS
摘要
Abstract
In order to improve the accuracy and efficiency of fault diagnosis for transformers and properly evaluate the corresponding statuses,relevance vector machine(RVM)is used to classify the overheating and discharge faults of transformers at first,then adaptive neural-fuzzy inference system(ANFIS)is utilized to identify the faults further,and estimate the probability of fault type.Experimental results show that the proposed method has a strong ability of learning and extracting features;especially,the method with fuzzy set and membership degree can output the probability of fault type in the case of overlapping fault features,which provides decision support for the status evaluation;the accuracy of the RVM-ANFIS method can reach as high as 96.15%,together with a higher calculation efficiency;compared with methods such as support vector machine(SVM)and artificial neural network(ANN),the proposed method has a better efficiency and a higher accuracy.关键词
变压器状态评估/溶解气体分析/相关向量机/自适应神经模糊推理系统/故障诊断Key words
transformer status evaluation/dissolved gas analysis/relevance vector machine(RVM)/adaptive neural-fuzzy inference system(ANFIS)/fault diagnosis分类
信息技术与安全科学引用本文复制引用
范竞敏,汪沨,孙秋芹,蒋勤稷,欧明辉..RVM和ANFIS用于变压器故障诊断及状态评估[J].电力系统及其自动化学报,2018,30(3):35-41,7.基金项目
国家自然科学基金资助项目(61102039) （61102039）
教育部新世纪优秀人才支持计划资助项目(NCET-11-0130) （NCET-11-0130）
湖南省自然科学基金资助项目(14JJ7029) （14JJ7029）
