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Fatigue distribution optimization for offshore wind farms using intelligent agent control

机译：使用智能代理控制的海上风电场疲劳分布优化

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摘要

A novel control approach is proposed to optimize the fatigue distribution of wind turbines in a large-scale offshore wind farm on the basis of an intelligent agent theory. In this approach, each wind turbine is considered to be an intelligent agent. The turbine at the farm boundary communicates with its neighbouring downwind turbines and organizes them adaptively into a wind delivery group along the wind direction. The agent attributes and the event structure are designed on the basis of the intelligent agent theory by using the unified modelling language. The control strategy of the intelligent agent is studied using topology models. The reference power of an individual wind turbine from the wind farm controller is re-dispatched to balance the turbine fatigue in the power dispatch intervals. In the fatigue optimization, the goal function is to minimize the standard deviation of the fatigue coefficient for every wind turbine. The optimization is constrained such that the average fatigue for every turbine is smaller than what would be achieved by conventional dispatch and such that the total power loss of the wind farm is restricted to a few percent of the total power. This intelligent agent control approach is verified through the simulation of wind data from the Horns Rev offshore wind farm. The results illustrate that intelligent agent control is a feasible way to optimize fatigue distribution in wind farms, which may reduce the maintenance frequency and extend the service life of large-scale wind farms.
机译：提出了一种基于智能代理理论的大型海上风电场风力发电机组疲劳分布优化控制方法。在这种方法中，每个风力涡轮机都被视为智能代理。农场边界处的涡轮机与其相邻的顺风涡轮机进行通信，并将它们沿风向自适应地组织为一个送风组。使用统一的建模语言，基于智能代理理论设计代理属性和事件结构。利用拓扑模型研究了智能代理的控制策略。重新分配来自风电场控制器的单个风力涡轮机的参考功率，以平衡功率分配间隔中的涡轮疲劳。在疲劳优化中，目标函数是使每个风力涡轮机的疲劳系数的标准偏差最小。优化受到限制，以使每个涡轮机的平均疲劳度小于常规调度所能达到的平均疲劳度，并且风电场的总功率损失被限制在总功率的百分之几。通过模拟Horns Rev海上风电场的风数据，验证了这种智能代理控制方法。结果表明，智能代理控制是优化风电场疲劳分布的一种可行方法，可以减少维护频率，延长大型风电场的使用寿命。

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  • 来源
    《Wind Energy》 |2012年第7期|p.927-944|共18页
  • 作者

    Rongyong Zhao; Wenzhong Shen; Torben Knudsen; Thomas Bak;

  • 作者单位

    CIMS Research Center, Tongji University, 200092 Shanghai, China;

    Department of Wind Energy, Technical University of Denmark, 2800 Lyngby, Denmark;

    Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark;

    Department of Electronic Systems, Aalborg University, 9220 Aalborg, Denmark;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    wind turbine; fatigue; offshore wind farm; intelligent agent; optimization;

    机译：风力发电机疲劳;离岸风电场;智能代理;优化;

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