用免疫BPSO算法和N-1原则多目标优化配置PMU

为了在满足全网的完全可观测的前提下实现PMU安装投入的性价比最高,通过理论分析得出判断电网节点拓扑可观测的依据,并提出以N-1可靠性检验原则对PMU配置方案进行冗余性检验,由此以全网完全可观测、PMU数目最少和N-1量测冗余度最高为目标建立了PMU多目标优化配置数学模型,并设计了一种结合免疫系统信息处理机制的二进制粒子群优化算法对模型进行求解。该算法综合了粒子群优化算法简单快速和免疫系统种群多样性的优点,明显改善了进化后期算法的收敛性能和全局寻优能力。对新英格兰39母线系统进行PMU多目标优化配置仿真及量测冗余性分析的结果表明,该法对PMU配置方案的量测可靠性及其所需PMU数量进行综合评价可方便快捷地得到性价比最优的方案,较之普通的PMU单目标优化配置方法更为合理和灵活。

Multi-objective Optimization for PMU Placement Based on Immune BPSO Algorithm and N-1 Reliability Test Principle

For the purpose of guaranteeing full observability of power grid and acquiring a good balance between the cost and the capability of phasor measurement unit (PMU) placement, the observability criteria of power grid nodes and the N-1 reliability principle to judge the measurement redundancy of placement schemes were proposed, and a mathematical model of multi-objective optimization for PMU placement problem was formed for both the minimum number of PMU to be equipped and the maximal measurement redundancy. An improved binary particle swarm optimization algorithm combined with the information processing mechanism of immune system was proposed to solve the multi-objective optimization problem. The proposed algorithm has both the properties of the speed advantage in binary particle swarm optimization and the diversity of antibodies in immune system, and the abilities of converging and seeking the global optimum results in later evolution process are improved observably. The number of PMU and the measurement reliability of PMU placement schemes were evaluated synthetically in the proposed multi-objective optimization method, and the optimal schemes could be rapidly achieved by the proposed method, which was demonstrated with PMU placement optimization simulation and measurement redundancy analysis using New England 39-bus system. The proposed method is more rational and flexible compared with those normal single-objective optimization methods for PMU placement.