摘要
针对电-气耦合系统在恶意攻击下的风险分析,提出了一种计及电-气耦合系统连锁故障的信息物理多阶段协同攻击策略。为了诱导调度人员做出错误调度决策和降低电网的安全裕度,提出了一种以最大化线路过载程度为目标的改进负荷重分配(LR)攻击模型。综合考虑天然气系统与电力系统的调度时间尺度差异,构建一种新型的电-气耦合系统多阶段协同攻击策略:初始阶段通过攻击气网侧气源或管道以影响电-气耦合节点的天然气机组状态,然后针对电力系统交替采用改进LR攻击和物理攻击,最终导致大规模连锁停运。基于Q-Learning提出了最优策略求解算法,以比利时20节点天然气系统和IEEE30节点系统为算例,验证了所提信息物理协同攻击模型的正确性和有效性。
As for the risk analysis of integrated electricity-natural gas system under malicious attack,the coordinated cyber-physical multi-stage attack strategy that takes into account the cascading failure of integrated electricity-natural gas system is proposed.Firstly,in order to mislead the operator and reduce the safety margin of power grid,an improved LR(Load Redistribution)attack model is proposed to maximize the degree of line overload.Secondly,considering the time-scale difference of dispatching between natural gas system and power system,a new coordinated multi-stage attack strategy for integrated electricity-natural gas system is constructed.In the initial stage,gas sources or pipelines in gas system are attacked to influence the state of natural gas generators at the integrated electricity-natural gas node.Then,the improved LR attack and physical attack are adopted alternately to the power system,which eventually leads to a blackout.Finally,a Q-Learning based algorithm is proposed to identify the optimal attack strategy.The correctness and validity of the proposed attack strategy are verified by the Belgium 20-node natural gas system and the IEEE 30-bus system.
作者
曹茂森
王蕾报
胡博
谢开贵
伏坚
温力力
周平
范璇
李博
曾意
CAO Maosen;WANG Leibao;HU Bo;XIE Kaigui;FU Jian;WEN Lili;ZHOU Ping;FAN Xuan;LI Bo;ZENG Yi(State Key Laboratory of Power Transmission Equipment & System Security and New Technology,Chongqing University,Chongqing 400044,China;State Grid Chongqing Economic Research Institute,Chongqing 401120,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2019年第8期128-136,共9页
Electric Power Automation Equipment
基金
国家自然科学基金资助项目(51725701)
关键词
多能源系统
电-气耦合系统
负荷重分配攻击
信息物理协同攻击
连锁故障
Q-Learning算法
multi-energy system
integrated electricity-natural gas system
load redistribution attack model
co-ordinated cyber-physical attack
cascading failure
Q-Learning algorithm