基于超球面支持向量机的SF6数字化表计数据异常检测

打开文本图片集

中图分类号：TP393 文献标志码：A 文章编号：1001-5922（2025）05-0186-04

Abstract：In order to make up for the problem of long SVM training time，a 1/4 hyperspherical SVM QSSVM model realizedby DBN was constructed to realizethe anomaly detection of the online testfunction of SF6 digital meter. Firstly，the DBN wasused to reduce the dimensionality of high-dimensional data，and then the analysis method of QSSVM and sliding window model was used to achieve efcient testing of abnormal problems.The results showed that the accuracy of QSSVM continued to improve when the window was expanded.With a window of 1Oo，QSSVM could reduce computation time by nearly half relative to SVM.When the sample dimension was increased，QSSVM still had excellent detection performance，and the detection rate was as high as 94.16% . This study is helpful to improve the anomaly detection ability of SF6 digital meter data，and has good practical promotion value Key words： wirelesssensor network ；gas anomaly detection；deep belief network ；support vector machine

目前最为广泛使用的仍然是机械式的SF6传统表计，具备很高的可靠度，可以确保满足测试精度要求，但并不能高效传输测试压力，无法对数据进行集中控制，不能对变电站实现自动管理[14]。（剩余4884字）