A Method for the Oil Chromatographic On-line Data Reconciliation Based on GSO and SVM

被引：0
|
作者：
Li, Min [1 ]
Yan, Xiaohu [2 ]
Cao, Yongxing [1 ]
Peng, Qian [1 ]
Zhang, Hailong [2 ]
机构：
[1] Sichuan Elect Power Res Inst, Chengdu 610072, Sichuan, Peoples R China
[2] State Grid Elect Power Res Inst, Wuhan 430074, Peoples R China
来源：
2013 IEEE TENCON SPRING CONFERENCE | 2013年
关键词：
Support Vector Machine; On-line Monitoring; Oil chromatogram; Data reconciliation; Glowworm swarm optimization algorithm; Parameters optimization; Neural network;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
To solve the problem of oil chromatographic on-line data distortion caused by outside environment and equipment error, a method for the oil chromatographic on-line data reconciliation based on glowworm swarm optimization (GSO) and support vector machine (SVM) is presented. Firstly, the important parameters that affect the performance of SVM are optimized through GSO. Secondly, SVM regression model is trained by some precise oil chromatographic off-line data. Then the oil chromatographic on-line data is reconciled by SVM regression model when the on-line data is abnormal. Finally, the feasibility and efficiency of the method proposed in the paper is confirmed by the oil chromatographic on-line and off-line data of the power transformer.
引用
收藏
页码：322 / 326
页数：5
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