基于遗传禁忌搜索算法的PMU布点配置

Optimal PMU placement based on genetic algorithm and tabu search algorithm

将遗传算法GA(Genetic Algorithm)和禁忌搜索算法TS(Tabu search)相结合,提出一种遗传禁忌搜索算法GATS(Genetic Algorithm & Tabu search)用于相量测量单元优化配置。GATS算法结合了遗传算法的随机搜索能力、并行性和禁忌搜索算法的记忆功能,有效地解决了遗传算法的爬山能力差、早熟的问题,提高了收敛速度及优化质量;同时遗传算法的种群操作,保留了遗传算法的多出发点的优势,弥补了禁忌搜索的单一单操作缺乏并行性的弱点。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GATS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。通过算例证明了算法的有效可靠。

This paper presents a hybrid algorithm which based on genetic algorithm and tabu search algorithm to optimize the phasor measurement units（PMU） placement problem. Genetic-Tabu Search combines the random searching ability and parallel of Genetic Algorithm with the memory function of Tabu Algorithm, and solves problems of Genetic Algorithm such as bad grade ability, Pre-maturity effectively, and enhances the convergence speed and utilization quality. Meanwhile, the category operations of Generic Algorithm keep the advantages of multi-origin of Generic Algorithm and amend the weak point of Tabu Searching which is the lack of parallel for single-single operation. It takes the infeasible and heuristic restoration while the constraint is handling. The Genetic-Tabu Search algorithm is applied to PMU placement optimization and fulfills the requirement of minimizing the number of PMUs in the system while the all node voltage phasor observable. A graph-theoretic procedure based on depth first search is adopted to analyze the system observability. Test results show that the GATS algorithm is effective.