基于超球面支持向量機的SF6數(shù)字化表計數(shù)據(jù)異常檢測

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中圖分類號：TP393 文獻標志碼：A 文章編號：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

目前最為廣泛使用的仍然是機械式的SF6傳統(tǒng)表計，具備很高的可靠度，可以確保滿足測試精度要求，但并不能高效傳輸測試壓力，無法對數(shù)據(jù)進行集中控制，不能對變電站實現(xiàn)自動管理[14]。（剩余4884字）