山东电力技术2025，Vol.52Issue(5)：50-56,7.DOI:10.20097/j.cnki.issn1007-9904.2025.05.006
基于改进PSO优化RBF神经网络的新型电力系统虚假数据攻击检测研究
Research on Detection of False Data Attack Detection Based on Improved PSO and Clustering Optimization RBF in Smart Grid
摘要
Abstract
As the next generation power system,the smart grid has achieved the goals of high efficiency,safety,economy,and environmental friendliness.However,the deep integration of information physical systems poses a new security risk of false data attacks.Therefore,a false data attack detection method based on improved particle swarm optimization(PSO)to optimize radial basis function(RBF)is proposed.Firstly,the mean clustering method is used to optimize the hidden layer center values of the RBF neural network to improve network performance.Then,by designing weight coefficients and learning factors,the convergence performance of the PSO algorithm is improved to optimize the field basis width and weight values of the RBF neural network,thereby improving the detection performance of the RBF neural network anomaly data detection model against false data attacks.Finally,compared with current methods such as PSO-RBF and artificial bee colony optimization RBF,numerical examples have verified that the detection algorithm proposed in this paper can improve the detection rate by at least 0.83%.关键词
虚假数据攻击/新型电力系统/改进PSO/聚类优化/RBFKey words
false data attacks/smart grid/improved PSO/clustering optimization/RBF分类
计算机与自动化引用本文复制引用
王新宇,张明月..基于改进PSO优化RBF神经网络的新型电力系统虚假数据攻击检测研究[J].山东电力技术,2025,52(5):50-56,7.基金项目
国家自然科学基金项目(62103357).National Natural Science Foundation of China(62103357). （62103357）
