基于VAE-WSVM的二次采样回路测试校核评估  

Sampling Loop Test Check Evaluation of Secondary System based on VAEWSVM Data Mining

作　　者：尹相国 刘慧娟 张冠育 毛玉荣 龚世玉 YIN Xiangguo;LIU Huijuan;ZHANG Guanyu;MAO Yurong;GONG Shiyu(State Grid Ningxia Electric Power Co.,Ltd.Ultra High Voltage Company,Yinchuan 750001,China;Wuhan Kemov Electric Co.,Ltd.,Wuhan 430223,China;College of Electrical Engineering and New Energy,Three Gorges University,Yichang 443002,China)

机构地区：[1]国网宁夏电力有限公司超高压公司,宁夏银川750001 [2]武汉凯默电气有限公司,湖北武汉430223 [3]三峡大学电气与新能源学院,湖北宜昌443002

出　　处：《中国电力》2025年第3期162-167,共6页Electric Power

基　　金：国家自然科学基金资助项目(52077120)。

摘　　要：分析二次系统采样回路中存在的隐患,针对现有方法难以获取大数据量的缺陷,基于历史测量数据,采用变分自编码器实现数据增殖,接着基于增殖后的数据集训练支持向量机模型,利用灰狼优化算法改进模型重要参数,并对二次系统采样回路误差范围进行分类校核。实验结果表明:所提方法训练的分类模型评估效果良好。Analyze the hidden dangers existing in the sampling circuit of the secondary system,and in view of the defects that the existing methods are difficult to obtain large amount of data,based on the historical measurement data,the variational autoencoder is used to achieve data proliferation,and then the support vector machine model is trained on the proliferated data set,and the important parameters of the model are improved by using the grey wolf optimization algorithm,and the error range of the secondary system sampling is classified and checked.The experimental results show that the classification model trained by the proposed method has a good evaluation effect.

关 键 词：二次系统采样回路 误差评估 小样本 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]