Prediction Method of Electric Shock Current Based on SVM and Neural Network Fusion Feedback

被引：0
|
作者：
Liu Y. [1 ,2 ]
Du S. [1 ]
Sheng W. [2 ]
机构：
[1] College of Information and Electrical Engineering, China Agricultural University, Haidian District, Beijing
[2] China Electric Power Research Institute, Haidian District, Beijing
来源：
Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 05期
关键词：
Detection method; Electric current; Fusion algorithm; Neural network algorithms; Residual current; SVM algorithm;
D O I：
10.13335/j.1000-3673.pst.2018.2977
中图分类号：
学科分类号：
摘要：
Identifying shock current signal and reverting from residual current in low voltage distribution network is a typical regression prediction problem. However, usually, the effect of mere utilization of simple regression forecasting methods such as support vector machine (SVM), neural network, is not ideal. In this paper, a fusion feedback method for electric shock current detection based on SVM and neural network is proposed. The method makes fusion determination, combining respective advantage of each model of SVM and neural network for integration analysis. Experiments are performed on real animals and plants to obtain training data and testing data. Results show that the fusion feedback method based on SVM and neural network can greatly improve accuracy of electric shock current signal detection. © 2020, Power System Technology Press. All right reserved.
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页码：1972 / 1977
页数：5
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