CAJ | 학술논문

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。
위료진일보제고동보상량측량단원(phasor measurement unit，PMU)배치적효솔이급기재대규모계통중적응용，제출일충결합인공지능방법화도론방법적혼합우화산법。해산법이4충탁복중구규칙화3충PMU배치규칙위기출，이용탁복약속분석법축보득출목표전망PMU배치적약속조건，유효축소료문제적가행해공간，병제출기우서호편마법적유전산법(genetic algorithm，GA)，사용개진적교차변이산자피면번식과정중출현불가행해，종이진행고효적우화。산례표명，소제산법불부능구준학득도최소PMU배치수목，이차대대계통야구유교쾌적운산속도，재대규모계통PMU배치중구유흔고적응용개치。
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.