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Performance analysis of different search algorithms in optimization of power system operation

机译:电力系统运行优化中不同搜索算法的性能分析

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A Flexible AC Transmission System (FACTS) device such as Static Var Compensator (SVC) in a power system improves the voltage profile, reduces power loss and overall cost of the system. To find optimal location and size of the FACTS device, different search algorithms are used. This study focuses on the comparative performance analysis of Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithm to find optimal location and rated value of SVC for the purpose of optimizing total power loss, total cost and improving voltage profile of the power system. Algorithms' computational efficiency is also investigated. To show validity of the proposed techniques, simulations are carried out on IEEE 14 bus and IEEE 57 bus.
机译:电力系统中的柔性交流输电系统(FACTS)设备(例如静态无功补偿器(SVC))可改善电压曲线,降低功率损耗并降低系统的整体成本。为了找到FACTS设备的最佳位置和大小,使用了不同的搜索算法。本研究着重于遗传算法(GA),粒子群优化(PSO)和蚁群优化(ACO)算法的比较性能分析,以找到SVC的最佳位置和额定值,以优化总功率损耗,总成本和改善电力系统的电压曲线。还研究了算法的计算效率。为了显示所提出技术的有效性,在IEEE 14总线和IEEE 57总线上进行了仿真。

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