摘要
针对智能电网调控系统通信和数据安全难以保障的问题,提出了一种基于改进支持向量机(SVM)的智能电网调控系统实时风险评估与预警技术.采用卷积神经网络(CNN)改进SVM模型得到CNN-SVM分类模型,用以处理实时风险评估体系中的数据信息.通过将CNN输出的数据特征输入SVM分类器进行风险等级分类,完成对数据中可能出现的风险进行识别、评估定级及预警.仿真结果表明,所提技术能够对调控系统实时风险进行准确、可靠的评估与预警,且其分类准确率、召回率、F1分数的均值分别为92%、86%和90%,均优于对比方法并具有更优的可靠性.
Aiming at the problem that it is difficult to ensure the communication and data security of smart grid regulation system,a real-time risk-assessment and early-warning technology of smart grid regulation system based on improved SVM was proposed.Convolutional neural network(CNN)was used to improve the support vector machine(SVM)model to obtain the CNN-SVM classification model,which was used to process the data information in the real-time risk-assessment system.By inputting the data features output by CNN into the SVM classifier for the classification of risk levels,the identification,evaluation,grading and early-warning of the risks that might appear in the data were completed.The simulation results show that the as-proposed technology can accurately and reliably evaluate and caution the real-time risk of regulation system.The mean values of classification accuracy,recall and F1 score are 93%,87%and 91%,respectively,which are better than those obtained by the reference method and facilitate better reliability.
作者
王宁
田家英
董宁
韩盟
陈艳霞
WANG Ning;TIAN Jia-ying;DONG Ning;HAN Meng;CHEN Yan-xia(School of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;Electric Power Research Institute,State Grid Beijing Electric Power Company,Beijing 100031,China;Power Dispatching Control Center,State Grid Beijing Electric Power Company,Beijing 100031,China)
出处
《沈阳工业大学学报》
CAS
北大核心
2022年第1期7-13,共7页
Journal of Shenyang University of Technology
基金
北京市自然科学基金资助项目(4164101)
国网北京市电力公司科技项目(52022319003R).
关键词
卷积神经网络
支持向量机
CNN-SVM模型
智能电网
调控系统
数据处理
风险评估预警
实时风险评估体系
convolutional neural network
support vector machine
CNN-SVM model
smart grid
regulation system
data processing
risk-assessment and early-warning
real-time risk-assessment system
