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Probabilistic Technique for Sizing FACTS Devices in Presence of Wind Farm Using NRGA Algorithm

机译:基于NRGA算法的风电场FACTS装置选型的概率技术。

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In this paper, simultaneous optimum allocation and sizing regarding three types of FACTS devices in presence of a wind power plant is considered using the NRGA algorithm. In order to make the simulation more realistic and increase the applicability of this article in the implementation for real situations different uncertainties related to the power system is considered. Uncertainties such as loads in power system, power plant power generation, transmission power lines outages, while the uncertainty index in this article is considered as N-2. Monte Carlo method, one of the probabilistic approaches in power system, is used to simulate uncertainties. By integrating the Monte Carlo and the K-means methods, convergence speed of the proposed method increased significantly. As the NRGAO algorithm is a multi-dimensional optimization algorithm for allocation and sizing TCSC, UPFC and SVC the two objective function in this paper are voltage profile improvement along with power line losses reduction. After determining and determining the optimal capacity of the Facts, the effects of their presence on the losses and network voltage profiles will be evaluated. A 24 IEEE RTS system is simulated in order to evaluate the proposed method.
机译:在本文中,使用NRGA算法考虑了同时存在三种类型的FACTS设备在风力发电厂中的同时最佳分配和规模。为了使仿真更加逼真并提高本文在实际应用中的适用性,考虑了与电力系统相关的各种不确定性。不确定性,例如电力系统的负荷,发电厂的发电,输电线路中断,而本文中的不确定性指数被认为是N-2。蒙特卡罗方法是电力系统中的一种概率方法,用于模拟不确定性。通过集成蒙特卡罗方法和K-means方法,该方法的收敛速度显着提高。由于NRGAO算法是用于分配和调整TCSC,UPFC和SVC尺寸的多维优化算法,因此本文中的两个目标函数是改善电压曲线和减少电力线损耗。在确定和确定事实的最佳容量之后,将评估它们的存在对损耗和网络电压曲线的影响。为了评估所提出的方法,对24个IEEE RTS系统进行了仿真。

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