基于改进自适应遗传算法的电力系统相量测量装置安装地点选择优化

将进化参数衰减因子与基于适应度变化的自适应遗传算法相结合,提出了一种新的自适应遗传算法,使遗传算法在进化过程中能够同时根据个体适应度和进化时间的变化自动调整交叉与变异概率,克服了原有自适应遗传算法易早熟的缺点,提高了最优解的多样性和寻优速度.精英个体保留策略保证了整个算法的全局收敛性.在约束条件处理时,采用了不可行解启发性修复方法,提高了算法的优化效果.基于图论的深度优先方法用于系统可观性分析.将新的自适应遗传算法应用于优化相量测量装置安装地点选择,实现了安装地点最少,而整个系统可观的目标.该算法已在某省46节点系统的优化计算中得到了验证.

A New Adaptive Genetic Algorithm and Its Application in Optimizing Phasor Measurement Units Placement in Electric Power System

This paper presents a new adaptive genetic algorithm to optimize the PhasorMeasurement Units (PMU) placement problem. The evolution attenuator factor is introduced into the genetic algorithm, which enables the new adaptive genetic algorithm to adjust the possibilities ofcrossover and mutation adaptively according to both the individual fitness and evolution generations. In the traditional adaptive algorithm, the high fitness solutions?possibilities of crossover and mutation are near zero in the early generations and the algorithm may get stuck at the near-optimal solution. The new adaptive algorithm keeps the crossover and mutation pressure to the high fitness solutions during the early and mid-term generations, which would introduce new solutions into the populations quicker and help the algorithm converge to global optimum. In the late generations, the attenuator factor decreases the possibilities of crossover and mutation sharply, thus the global optimal solutions are protected from disruption. The new adaptive genetic algorithm is applied to PMU placement optimization and fulfills the requirement of minimizing the number of PMUs in the system while keeping the all nodes voltage phasor observable. A graph-theoretic procedure based on depth first search is adopted to analyze thesystem observability. Illustrative results on the IEEE 14-bus system and a provincial 46-bus system are provided.