考虑拓扑约束并采用改进遗传算法的PMU优化配置

Improved Genetic Algorithm Based Optimal Configuration of PMUs Considering Topological Constraints

为了进一步提高同步相量测量单元(phasor measurement unit,PMU)配置的效率以及其在大规模系统中的应用,提出一种结合人工智能方法和图论方法的混合优化算法。该算法以4种拓扑重构规则和3种PMU配置规则为基础,利用拓扑约束分析法逐步得出目标电网PMU配置的约束条件,有效缩小了问题的可行解空间,并提出基于序号编码法的遗传算法(genetic algorithm,GA),使用改进的交叉变异算子避免繁殖过程中出现不可行解,从而进行高效的优化。算例表明,所提算法不仅能够准确得到最小PMU配置数目,而且对大系统也具有较快的运算速度,在大规模系统PMU配置中具有很高的应用价值。

To further improve the efficiency of configuring synchronous phasor measurement units（PMUs） and promote its application in large-scale power system, a hybrid optimization algorithm integrated with artificial intelligence and graph theory is proposed. Based on four topological reconfiguration rules and three PMU configuration rules, the constraints for PMU configuration in the power grid is attained gradually utilizing topological constraint analysis, and the feasible solution space is effectively reduced. A genetic algorithm（GA） based on serial-number coding is proposed. Meanwhile the improved crossover and mutation operators are utilized to avoid the appearance of infeasible solutions during the multiplicative process. Thus the high-efficient optimization can be performed. Simulation results of IEEE 30-bus system, IEEE 57-bus system and IEEE 118-bus system show that using the proposed algorithm not only the configuration scheme with minimized number of PMUs can be obtained accurately, but also a higher computational speed can be achieved in large-scale power grid.