综合智慧能源2025，Vol.47Issue(9)：18-27,10.DOI:10.3969/j.issn.2097-0706.2025.09.003
基于核极限学习机的城市电网信息物理系统安全态势预警
Early warning of security situation for cyber-physical systems of urban power grids based on kernel extreme learning machine
摘要
Abstract
Timely early warning of security situation in cyber-physical systems(CPS)of urban power grids is critical for ensuring safe and stable operation.To address the early-warning challenges for the operational status of CPS under multiple disturbances,a security situation early warning method based on kernel extreme learning machine(KELM)was proposed.A coupling model of the physical and information layers of the power grid was established by integrating cellular automata theory,and the mechanism of cross-space risk propagation was analyzed;An ensemble KELM early warning model was developed,in which multidimensional data were deeply integrated through radial basis function kernel mapping,and prediction accuracy was enhanced by the ensemble structure;An early warning indicator system was established,and indicator weights were dynamically allocated using the entropy weight method to classify early warning levels of security situation.Simulation experiments based on the IEEE 33-bus distribution network demonstrated that,under distributed generation integration scenarios,the proposed method achieved a 12.49%reduction in mean squared error of voltage fluctuation prediction compared to traditional extreme learning machine methods,verifying the efficiency and robustness of the model.关键词
城市电网信息物理系统/安全态势预警/风险跨空间传播/元胞自动机/核极限学习机Key words
cyber-physical system of urban power grid/security situation early warning/cross-space risk propagation/cellular automata/kernel extreme learning machine分类
信息技术与安全科学引用本文复制引用
许傲,王子月,徐俊俊,周宪..基于核极限学习机的城市电网信息物理系统安全态势预警[J].综合智慧能源,2025,47(9):18-27,10.基金项目
国家自然科学基金项目(52107101)National Natural Science Foundation of China(52107101) （52107101）
