基于节点概率可观性的PMU多目标优化配置  

Multi-objective Optimal PMU Placement Based on Node Probabilistic Observability

作　　者：罗深增 LUO Shenzeng(Central China Branch of State Grid Corporation of China,Wuhan,Hubei 430077,China)

机构地区：[1]国家电网有限公司华中分部,湖北武汉430077

出　　处：《广东电力》2023年第12期39-46,共8页Guangdong Electric Power

摘　　要：针对相量测量单元(phasor measurement unit,PMU)的多目标优化配置(multi-objective optimal PMU placement,MOPP)问题,提出一种计及节点概率可观性的MOPP模型。从节点可观的角度,考虑PMU及线路的故障概率,计算节点失去可观性的概率。在进行PMU的优化配置时,同时考虑PMU安装数量最少和各节点失去可观性的平均概率最小2个优化目标,采用基于快速非支配排序策略的多目标生物地理学优化(multi-objective biogeography-based optimization,MOBBO)算法进行求解得到Pareto解集,并利用模糊决策理论得到最优折衷解。IEEE 14和57节点系统的大量仿真结果表明,所提方法可以在MOPP中差异化计及PMU以及支路故障概率的影响,与非支配排序遗传算法相比,MOBBO在求解MOPP模型中能够得到更为逼近Pareto前沿的解,提供更佳的决策参考方案。Aiming at the problem of multi-objective optimal phasor measurement unit placement(MOPP),this paper proposes a MOPP model considering node probabilistic observability.From the perspective of the node observability reliability,the paper considers the failure probability of PMU and transmission lines and calculates the probability of node losing observability.The proposed optimal PMU configuration method takes into consideration minimal numbers of PMUs and the average probability of nodes losing observability for PUM optimal configuration.It uses the multi-objective Biogeography-based optimization with fast non-dominated sorting strategy to solve the multi-objective optimization problem and obtain Pareto optimal set.Meanwhile,the fuzzy decision theory is utilized to get the best compromise solution.Extensive simulation results analysis of IEEE 14 and IEEE 57 bus systems indicate that the proposed method can consider the impact of different failure probabilities of PMU and branches in MOPP.Compared with non-dominated sorting genetic algorithm(NSGA-Ⅱ),the multi-objective Biogeography-based optimization can obtain more Pareto frontier approaching solutions in solving the MOPP model and provide better decision-making reference solutions.

关 键 词：PMU优化配置 多目标优化 节点概率可观性 生物地理学优化 

分 类 号：TM715[电气工程—电力系统及自动化]