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Optimal STATCOM controller for enhancing wind farm power system performance under fault conditions

机译:最佳Statcom控制器,用于在故障条件下提高风电场电力系统性能

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Harnessing wind energy for producing electrical power becomes vital and essential now a day in our communities. However the large penetration of wind farms into the electrical power grid affects the dynamic performance of grid especially during abnormal conditions. STATCOM FACTS device is employed for mitigation the voltage sage and improving power quality at the point of common coupling (PCC) where large scale wind power plants (WPP) are connected to grid during faults period. PI regulators with classical gain tuning are the most common procedure used for controlling the STATCOM performance. In this paper the particle swarm optimization (PSO) and ant colony optimization (ACO) as evolutionary methods are adopted to obtain flexible and reliable PI parameter tuning for cogging and improving STATCOM dynamic behavior during voltage sage occurrence. The main contribution of using STATCOM is to obtain an adequate fault ride through for wind driven doubly fed induction generator that can track the grid code requirements. So the impacts of deep voltage sage introduced to 9 MW wind farm DFIG and connected to 120 kV power grid are investigated by using Matlab/Simulink program. Results obtained show the effectiveness of the proposed PSO and ACO methodologies.
机译:利用风能为生产电力而变得至关重要,现在是我们社区的一天。然而,风电场进入电力电网的大渗透会影响栅格的动态性能,特别是在异常条件下。 STATCOM事实器件用于减轻电压鼠标并提高公共耦合点(PCC)的功率质量,其中大规模风电站(WPP)在故障时段期间连接到电网。具有经典增益调整的PI调节器是用于控制Statcom性能的最常用程序。在本文中,采用粒子群优化(PSO)和蚁群优化(ACO)作为进化方法,以获得用于齿槽的灵活可靠的PI参数调整,并在电压鼠尾队发生期间提高Statcom动态行为。使用STATCOM的主要贡献是获得足够的故障乘坐,用于风驱动双馈电流发电机,可以跟踪网格代码要求。因此,通过使用MATLAB / SIMULINK程序研究了引入9 MW风电场DFIG和连接到120kV电网的深度电压SAGE的影响。获得的结果显示了所提出的PSO和ACO方法的有效性。

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