高压电器2024，Vol.60Issue(7)：173-181,9.DOI:10.13296/j.1001-1609.hva.2024.07.019
基于多分类支持向量机的变压器在线监测数据错误模式识别
Error Pattern Identification of On-line Monitoring Data of Transformer Based on Multi-classification SVM
摘要
Abstract
In view of online monitoring data quality of dissolved gas in transformer oil,the annual data of more than 200 monitoring devices in 2020 are counted and three main data error patterns are summarized.The identification strategy and characteristic parameters of data errors pattern are proposed,and a multi-classification support vector machine is constructed to identify and classify the data errors.The Kernel principal component analysis and permuta-tion and combination traversal method are used to reduce dimension of the feature vector.The identification accura-cy of the constructed multi-classification support vector machine classifier for H2 error data is 97.5%,and for other gasses is more than 90%.The constructed classifier is used to make statistics on the annual data of 2020,in which the error data of H2 and C2H2 reach 27.14%and 1.75%respectively.关键词
错误数据/模式识别/支持向量机/在线监测/变压器/油中溶解气体分析Key words
error data/pattern identification/support vector machines/online monitoring/transformer/dissolved gas analysis引用本文复制引用
何宁辉,吴旭涛,张佩,沙伟燕,周秀,丁培,杨擎柱,程养春..基于多分类支持向量机的变压器在线监测数据错误模式识别[J].高压电器,2024,60(7):173-181,9.基金项目
国家电网科学技术项目(5229DK19004Z). Project Supported by Science and Technology Project of SGCC(5229DK19004Z). （5229DK19004Z）
