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Application of non-dominated sorting genetic algorithm (NSGA-II) in siting and sizing of wind farms and FACTS devices for optimal power flow in a system

机译:非支配排序遗传算法(NSGA-II)在风电场和FACTS设备选址和规模确定中的应用,以实现系统中的最佳功率流

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This paper presents the effective siting and sizing of the Wind Farms (WF) and Flexible AC Transmission Systems (FACTS) devices in a power system applying the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The objectives which are simultaneously considered in NSGA-II are: a) reduction of costs, and b) improvement of voltage profile of the buses in the system under study. In the simulations, the optimal siting and sizing of WF has been investigated along with FACTS devices' best siting and setting in the power system under study. K-means clustering algorithm is applied in classifying the data related to the WF output power and load power demand in 8760 hours of a year. The simulation results are compared with each other and at last, best solutions are introduced. IEEE Reliability Test System (IEEE-RTS) is used as the test system for investigation of system parameters and for comparison of the proposed solutions with each other.
机译:本文介绍了使用非支配排序遗传算法-II(NSGA-II)的电力系统中风电场(WF)和柔性交流输电系统(FACTS)设备的有效选址和选型。在NSGA-II中同时考虑的目标是:a)降低成本,以及b)改善所研究系统中总线的电压分布。在仿真中,已研究了WF的最佳选址和选型,以及FACTS设备在所研究的电力系统中的最佳选址和设置。 K-means聚类算法用于对一年8760小时内与WF输出功率和负载功率需求相关的数据进行分类。将仿真结果相互比较,最后介绍最佳解决方案。 IEEE可靠性测试系统(IEEE-RTS)用作测试系统,用于调查系统参数并相互比较建议的解决方案。

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