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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:非统治分类遗传算法(NSGA-II)在系统中最优功率流的拟统治和尺寸的选址和尺寸

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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:本文介绍了在应用非主导分类遗传算法-II(NSGA-II)的电力系统中的风电场(WF)和柔性AC传输系统(事实)设备的有效选址和尺寸。在NSGA-II中同时考虑的目标是:a)降低成本,b)在研究中的系统中公共汽车的电压曲线的改进。在模拟中,已经调查了WF的最佳选址和尺寸,并在研究中的电力系统中的最佳选址和设置。 K-Means聚类算法应用于在8760小时内分类与WF输出电源和负载电源需求相关的数据。仿真结果彼此比较,最后介绍了最佳解决方案。 IEEE可靠性测试系统(IEEE-RTS)用作系统参数调查的测试系统,以及相互比较提出的解决方案。

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