Review of research on situation awareness in resilient power systems

doi:10.3969/j.issn.2097-0706.2025.03.001

Abstract

Abstract:

As global energy demand continues to rise， achieving a green and low-carbon energy transition has become the core task of energy systems. Ensuring the stability and security of power systems is particularly critical during this transition. Facing the challenges of natural disasters and human factors， the development of resilient construction in power systems under extreme events is of paramount importance. In this review， the definition， characteristic curves， resilience assessment methods of resilient power systems and the overall framework of cyber-physical power systems（CPPS） are explored in depth. Based on a review of the literature， the situation awareness（SA） technology in resilient power systems is comprehensively analyzed， especially grid SA and network SA. Combined with research results of the coupling relationship of CPPS， the SA framework of CPPS is constructed， and the collaborative SA of information side and physical side is studied to improve the resilience of power system in the face of climate change and energy challenges. The future research focus is also proposed.

Key words: power systems, resilience assessment, cyber-physical power systems（CPPS）, situational awareness （SA）, energy transition

CLC Number:

TK01

ZHANG Lu, LU Anshan, HUA Yang, LIU Tianhao, ZHANG Dongdong. Review of research on situation awareness in resilient power systems[J]. Integrated Intelligent Energy, 2025, 47(3): 1-14.

Figures/Tables 8

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方法		优点	缺点
主观权重法	专家法层次分析法模糊评价法	能够反映决策主体的意图	主观随意性较大，可能导致决策结果的波动
客观权重法	熵权法变异系数法主成分分析法	在数学理论上具有很强的合理性	可能无法充分体现决策者的意图
组合权重法		准确性高、鲁棒性强、灵活性高、充分利用信息、减少过拟合	计算复杂、权重确定困难、模型选择复杂、维护成本高、可能有滞后性

维度	数据来源	采集技术
资产	系统配置信息、网络拓扑、服务、用户数据等	Wireshark，NetFlow，SNMP
漏洞	软件漏洞、配置漏洞、结构漏洞等	CNNVD，CVE 等漏洞数据库
威胁	IDS告警、防火墙、日志数据、流量数据等	Flume，Syslog，蜜罐，张量分析

类型	方法	优点	缺点	文献
基于数学模型	层次分析法、集对分析法、模糊综合评价法、距离偏差法、多属性效用函数法	数学表达式清晰明确，进而给出确定性结果，应用广泛	涉及的主观因素较多，可能导致缺少客观统一的标准	[77⇓-79]
基于概率和知识推理	模糊推理、贝叶斯网络、马尔可夫博弈过程、Dempster-Shafer证据理论	采用逻辑推理进行评估	操作困难，大量的规则和知识占用大量空间，推理过程复杂	[80⇓-82]
基于模式分类	聚类分析、粗糙集、灰色关联分析、神经网络法、支持向量机法	学习能力强，模型建立较为准确	计算量过大，建模时间较长，特征数量较多且不易理解	[83⇓⇓-86]