摘要
电力网络与信息网络深度融合是实现电网完全可观可控的基础,然而实际的电力网络与信息网络并没有形成一一对应关系,所以难以实现电网的全面感知。因此,提出了一种基于电力网络拓扑结构的信息网的局部演化模型,该模型包含信息节点和通信链路的新建与升级改造,其新建节点与链路以生长演化的连接方式接入信息网络。此外,基于部分相依电力信息物理系统连锁故障传播模型,以负荷损失的互补累积概率作为标准,来评价电力网络和不同重连概率的信息网络交互作用的可靠性。以某省220 kV及以上的电网系统为例来进行仿真分析,验证了信息网局部演化模型的有效性和鲁棒性,并通过构建信息网的局部生长演化模型提高了电网信息物理系统抵御连锁故障的能力。
The deep integration of power grid and information network is the basis for realizing the complete observability and controllability of power grid.However,the actual power grid and information network do not form a one-to-one correspondence,so it is difficult to realize the comprehensive perception of power grid.In this paper,a local evolution model of information network based on physical power grid topology is proposed.The model includes the construction,upgrading and transformation of information nodes and communication links.The new nodes and links are connected to the information network in the connection mode of growth evolution model.In addition,based on the cascading fault propagation model of partially dependent power cyber-physical system,this paper takes the complementary cumulative probability of load loss as the standard to evaluate the reliability of the interaction between physical power grid and information network with different reconnection probabilities.Taking a 220 kV and above power grid system in a province as an example,the effectiveness and robustness of the local evolution model of the information network are verified,and the ability of the power grid cyber-physical system is improved to resist cascading faults by constructing the local growth evolution model of the information network.
作者
陈洋
蔡晔
汤丽
曹一家
刘颖
CHEN Yang;CAI Ye;TANG Li;CAO Yijia;LIU Ying(College of Electrical and Information Engineering,Changsha University of Science&Technology,Changsha 410114)
出处
《电气工程学报》
CSCD
2022年第3期162-169,共8页
Journal of Electrical Engineering
基金
国家自然科学基金联合基金(U1966207)
湖南省自然科学基金(2020JJ5573)
国家自然科学基金(51807010)
长沙理工大学研究生科研创新(SJCX202052)资助项目。
关键词
电力信息物理系统
演化模型
连锁故障
Power cyber-physical system
evolution model
cascading fault