基于二元猫群算法的电力系统完全可观测性PMU优化配置

Ankur Srivastava, Sydulu Maheswarapu
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引用次数: 19

摘要

提出了一种基于二元猫群优化(BCSO)的元启发式算法。BCSO是猫群优化算法(Cat Swarm Optimization, CSO)的二进制版本,基于对猫行为的观察。BCSO有两种工作模式:寻道模式和跟踪模式。该方法用于电力系统完全可观测性的相量测量单元(pmu)的最佳配置。该优化问题的目标是在保持系统的可观察性不变的情况下,使所需的pmu总数最小化。在IEEE 14总线和IEEE 30总线测试系统上对该算法进行了测试,并给出了测试结果。并利用二元粒子群优化、广义整数线性规划和基于有效数据结构的算法等现有的传统算法和元启发式算法验证了上述结果。本文首次将二元猫群优化算法应用于pmu的优化配置问题。
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Optimal PMU placement for complete power system observability using Binary Cat Swarm Optimization
This paper presents a meta-heuristic algorithm using Binary Cat Swarm Optimization (BCSO). BCSO is binary version of Cat Swarm Optimization (CSO) based on the observation of the behavior of cats. BCSO has two modes of operation: seeking mode and tracing mode. This proposed methodology is used for the optimal placement of phasor measurement units (PMUs) for complete observability of a power system. The objective of this optimization problem is the minimization of the total number of PMUs required by keeping the observability of the system intact. The proposed algorithm was examined with IEEE 14-bus and IEEE 30-bus test system and their results are presented in the paper. These results were also verified with different existing conventional and meta-heuristic algorithms such as binary particle swarm optimization, generalized integer linear programming and effective data structure based algorithm. It is first time that the proposed binary cat swarm optimization has been contemplated for the problem of optimal placement of PMUs.
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