论文标题
基于社会层次结构的分布式经济模型预测浮动海上风电场
Social Hierarchy-based Distributed Economic Model Predictive Control of Floating Offshore Wind Farms
论文作者
论文摘要
本文实施了最近开发的基于社会等级的分布式经济模型预测控制（DEMPC）算法，以实现最大化的目的。控制器使用偏航和基于感应的涡轮机重新定位（Yitur）实现了这一目标，该涡轮机重新定位（Yitur）最大程度地减少了相邻的浮动风力涡轮转子之间的重叠区域，以最大程度地减少尾流效果。浮动风电场动态和性能是使用Fowfsim-Dyn进行数值预测的。为了确保DEMPC算法的快速决策，在动态优化过程中，使用前馈神经网络来估计风力涡轮机动力学。对于一个布局的模拟风电场范围从1 x-2到1 x 1不等，当使用Yitur而不是贪婪的操作时，预计能源生产将增加20％。还研究了风速和方向的变化增加，并且由于神经网络预测的误差的增加而被证明会降低控制器的性能。
This paper implements a recently developed social hierarchy-based distributed economic model predictive control (DEMPC) algorithm in floating offshore wind farms for the purpose of power maximization. The controller achieves this objective using the concept of yaw and induction-based turbine repositioning (YITuR), which minimizes the overlap areas between adjacent floating wind turbine rotors in real-time to minimize the wake effect. Floating wind farm dynamics and performance are predicted numerically using FOWFSim-Dyn. To ensure fast decision-making by the DEMPC algorithm, feed-forward neural networks are used to estimate floating wind turbine dynamics during the process of dynamic optimization. For simulated wind farms with layouts ranging from 1-by-2 to 1-by-5, an increase of 20% in energy production is predicted when using YITuR instead of greedy operation. Increased variability in wind speed and direction is also studied and is shown to diminish controller performance due to rising errors in neural network predictions.
