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

doi:10.3969/j.issn.2097-0706.2025.09.003

摘要/Abstract

摘要：

城市电网信息物理系统（CPS）安全态势的及时预警是保证其安全稳定运行的关键。针对多元扰动下城市电网CPS运行态势预警难题，提出了一种基于核极限学习机（KELM）的安全态势预警方法。结合元胞自动机理论构建电网物理层与信息层耦合模型，探讨安全风险跨空间传播机理；采用集成KELM预警模型，通过径向基核函数映射实现多维数据深度融合，并通过集成结构提升预测精度；构建预警指标体系，并根据熵权法动态分配指标权重，划分安全态势预警等级。基于IEEE 33节点配电网的仿真试验表明，所提方法在分布式电源接入场景下，电压波动预测准确率的均方误差较传统极限学习机方法降低了12.49%，验证了模型的高效性与鲁棒性。

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

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.

Key words: cyber-physical system of urban power grid, security situation early warning, cross-space risk propagation, cellular automata, kernel extreme learning machine

中图分类号:

TM73：TK01

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.

XU Ao, WANG Ziyue, XU Junjun, ZHOU Xian. Early warning of security situation for cyber-physical systems of urban power grids based on kernel extreme learning machine[J]. Integrated Intelligent Energy, 2025, 47(9): 18-27.

图/表 14

图1

图2

表1

图3

图4

图5

表2

图6

表3

表4

城市电网CPS安全态势风险预警等级阈值

预警等级	阈值
0 级	$S (t) < 0.2$
Ⅰ级	$0.2 ≤ S (t) < 0.5$
Ⅱ级	$0.5 ≤ S (t) < 0.8$
Ⅲ级	$S (t) ≥ 0.8$

表4

图7

表5

图8

图9

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预警等级	电网状态
0 级（电网安全）	各电网状态处于正常波动范围内
Ⅰ级（蓝色预警）	电网指标超出波动范围，存在安全风险
Ⅱ级（黄色预警）	多个指标超过阈值，电网安全风险较大
Ⅲ级（红色预警）	大范围节点异常、多条线路严重过载、失负荷风险过大

节点	介数中心性	脆弱性指数	综合得分	节点重要性
1	0.12	0.92	0.65	次关键节点
12	0.15	0.85	0.53	次关键节点
18	0.35	0.88	0.82	关键节点
22	0.30	0.80	0.74	关键节点
25	0.28	0.75	0.68	次关键节点

模型	MSE	R²
ELM	0.016 093	0.976 53
BP神经网络	0.017 548	0.974 41
XGBoost	0.028 044	0.959 10
KELM	0.014 082	0.979 46

预警等级	时间段	占比/%
0 级	00:00 — 08:00, 14:00 —16:00， 20:00 — 24:00	66.7
Ⅰ级	09:00 —13:00	20.8
Ⅱ级	17:00 —19:00	12.5
Ⅲ级		0