基于RBF神经网络的电力系统网络安全风险评估  

Power System Network Security Risk Assessment Based on RBF Neural Network

作　　者：余云昊 狄查美玲 郭翔 YU Yunhao;DI-ZHA Meiling;GUO Xiang(Dispatching Control Center of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,Guizhou,China)

机构地区：[1]贵州电网有限责任公司调度控制中心,贵州贵阳550002

出　　处：《电力大数据》2024年第11期40-47,共8页Power Systems and Big Data

基　　金：中国南方电网有限责任公司科技项目(066500KK52210038)。

摘　　要：为提高电力系统网络安全风险评估的精度与效率,本文提出一种基于RBF神经网络的电力系统网络安全风险评估方法。首先,建立一个安全风险评价指标体系,并将其与风险评价矩阵相结合;然后,通过计算指标的信息熵,确定评价指标的风险等级,并将其分为五个风险等级;最终,计算及输入网络安全风险分布融合特征值,并以电力系统网络安全风险评估结果为依据,采用RBF神经网络构建电力系统网络安全风险评估模型。实验证明,该方法达到预期目标,最高安全风险评估,精确度为96%,评估效率达到98%。In order to improve the accuracy and efficiency of power system network security risk assessment,this paper proposes a method of power system network security risk assessment based on RBF neural network.Firstly,establish a security risk assessment index system,and combine it with the risk assessment matrix.By calculating the information entropy of the index,the risk level of the evaluation index is determined and divided into five risk levels.Finally,the network security risk distribution fusion eigenvalues are calculated and input,and based on the network security risk assessment results of power system,RBF neural network is used to construct the network security risk assessment model of power system.Experiments have shown that the method achieves the desired goal,96%accuracy in the assessment of the highest security risk,and 98%efficiency in the assessment.

关 键 词：RBF网络 电力系统 网络安全风险 安全性评价 安全性等级 

分 类 号：TM615[电气工程—电力系统及自动化]