基于NSGA-Ⅱ的电力信息物理系统骨干网络辨识  

Backbone Network Identification of Cyber-Physical Power System Based on Non-dominated Sorting Genetic Algorithm-Ⅱ

作　　者：蔡晔 汤丽 唐夏菲 陈洋 曹一家 CAI Ye;TANG Li;TANG Xiafei;CHEN Yang;CAO Yijia(School of Electrical and Information Engineering,Changsha University of Science&Technology,Changsha 410114,China)

机构地区：[1]长沙理工大学电气与信息工程学院,湖南省长沙市410114

出　　处：《电力系统自动化》2023年第12期38-46,共9页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(52277076);国家自然科学基金委-国家电网有限公司智能电网联合基金资助项目(U1966207)。

摘　　要：针对实际存在一一对应的电力信息物理系统,辨识其抗灾型骨干网架并进行加固,可提高电力信息物理系统在面对自然灾害或网络攻击下的可靠性。文中提出骨干网络辨识多目标优化模型,所提模型综合考虑整个系统的经济性、抗毁性和恢复性,并满足重要负荷约束、网络连通性约束、网络规模约束和潮流约束。首先,使用改进的非支配排序遗传算法(NSGA-Ⅱ)求解多目标优化模型,并利用多目标决策中的熵权法给各个目标函数赋权重。然后,使用逼近理想解排序法筛选出帕累托解集中的最优解。最后,以IEEE 39节点系统和中国某地区500 kV实际电网为例,验证了所提的电力信息物理系统骨干网络辨识算法的有效性。For the actual cyber-physical power system with one-to-one correspondence,identifying and reinforcing its disaster-resistant backbone grid can improve the reliability of the cyber-physical power system under natural disasters or cyber attacks.Therefore,this paper proposes a multi-objective optimization model for backbone network identification.The proposed model comprehensively considers the economy,anti-destruction and recovery of the entire system,and satisfies important load constraints,network connectivity constraints,network scale constraints,and power flow constraints.First,the multi-objective optimization model is solved by using the improved non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ),and each objective function is assigned a weight by using the entropy weight method in multi-objective decision-making.Then,the optimal solution in the Pareto solution set is filtered by using the approximately ideal solution ranking method.Finally,the effectiveness of the proposed backbone network identification algorithm for the cyber-physical power system is verified by taking the IEEE 39-bus system and an actual 500 kV power grid in a region of China as examples.

关 键 词：电力信息物理系统 骨干网络 改进的非支配排序遗传算法 多目标优化 熵权-TOPSIS 

分 类 号：TM73[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]