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Research on method of wind power clustering trend based on sub-state reconstruction

机译:基于子状态重构的风电聚类趋势方法研究

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Since the fluctuating characteristic of wind energy, with the increase of wind farms clustering size, the fluctuation of wind power gradually decreases and shows “convergence effect”. Being of guiding significance, it is important to grasp the trend of the convergence effect for transmission capacity configuration of large-scale wind power network. In this paper, different scenes of wind power output are defined, meanwhile, the convergence characteristics of wind power in each scene are analyzed. By fitting the duration curve in each scene, a scene reconstruction method based on convergence characteristics analysis is proposed. The validity of the reconstruction method is tested with measured data. Simulation case shows that the trend of convergence for wind power can be analyzed more accurately by the proposed sub-scene reconstruction method. The amount of scenes affects the fitting accuracy of duration curve, comparing to the scene divided by 5, 10 and 20, reconstruction curve divided by 10 scenes is more accurate for the description of wind convergence trend.
机译:由于风能的波动特性,随着风电场群规模的增加,风电波动逐渐减小,呈现出“收敛效应”。具有指导意义,对于大规模风电网络的输电容量配置,掌握收敛效应的趋势很重要。本文定义了风电输出的不同场景,同时分析了每个场景中风电的收敛特性。通过拟合每个场景的持续时间曲线,提出了一种基于收敛性分析的场景重构方法。用实测数据测试了重建方法的有效性。仿真实例表明,所提出的子场景重构方法可以更准确地分析风电的收敛趋势。场景数量会影响持续时间曲线的拟合精度,与现场除以5、10和20相比,重建曲线除以10个场景对于描述风的收敛趋势更为准确。

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