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Wind speed modeling for cascade clusters of wind turbines part 1: The cascade clusters of wind turbines

机译:风力涡轮机级联簇的风速建模第1部分:风力涡轮机的级联簇

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摘要

Wind energy conversion efficiency has always been an important issue for wind farms. And wind speed calculation is the basic task and key work of wind energy conversion optimization. The cascade clusters of wind turbines are directly related to wind speed, and affected by the terrain, wake disturbance, location distribution and other factors. So it is very difficult to adopt parameter modeling. The cascade characteristics among cluster wind turbines (WTs) are embodied in historical operation data of the WTs. Taking the input wind direction as the initial parameter, we construct the WTs location correlation matrix of the neighborhood distribution relationship of WTs location; we then obtain the correlation relationship of the WTs production wind speed and power by combining the WTs production monitoring data. At the same time, "coupling element" and "aggregation element" WTs can be obtained from the cascade clusters. By verifying the data of a large wind farm, the model proposed in this paper clarifies the relationship between the wind speed and the cascade clusters; using this model, we can calculate the cluster distribution under different wind conditions. It is highly practical and can be applied to other wind farms to support formulation of the efficiency optimization strategies.
机译:风能转换效率一直是风电场的重要问题。风速计算是风能转换优化的基本任务和关键工作。风力涡轮机的级联簇与风速直接相关,并受到地形,唤醒干扰,位置分布和其他因素的影响。所以很难采用参数建模。簇风力涡轮机(WTS)之间的级联特性体现在WTS的历史操作数据中。以输入风向为初始参数,我们构建WTS位置的邻域分布关系的WTS位置相关矩阵;然后,我们通过组合WTS生产监测数据来获得WTS生产风速和功率的相关关系。同时,可以从级联簇获得“耦合元素”和“聚合元素”WTS。通过验证大型风电场的数据,本文提出的模型阐明了风速和级联簇之间的关系;使用此模型,我们可以在不同的风力条件下计算集群分布。它非常实用,可应用于其他风电场,以支持制定效率优化策略。

著录项

  • 来源
    《Energy》 |2020年第15期|118097.1-118097.9|共9页
  • 作者单位

    School of Energy Power and Mechanical Engineering North China Electric Power University Beinong Road 2# Beijing W2206 PR China;

    School of Energy Power and Mechanical Engineering North China Electric Power University Beinong Road 2# Beijing W2206 PR China;

    School of Energy Power and Mechanical Engineering North China Electric Power University Beinong Road 2# Beijing W2206 PR China;

    School of Energy Power and Mechanical Engineering North China Electric Power University Beinong Road 2# Beijing W2206 PR China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Cascade clusters of wind turbines; Cascade characteristics; Spectral clustering; Segmentation algorithm;

    机译:级联风力涡轮机簇;级联特征;光谱聚类;分割算法;

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