Toward stochastic dynamical wake-modeling for wind farms
Toward stochastic dynamical wake-modeling for wind farms
复制标题
风电场随机动态尾流建模
DOI：
10.23919/acc53348.2022.9867678
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
A. Zare
中科院分区：
文献类型：
--
作者：
Aditya Bhatt;A. Zare
传统上，涡轮机尾迹的低保真分析模型被用来演示先进控制算法在增加风力发电场年发电量方面的效用。然而，在实践中，使用基于传统低保真模型的闭环策略来实现显著的性能改进仍然具有挑战性。这是由于模型对涡轮机之间复杂的空气动力学相互作用的不可知的尾流预测过于简化的静态性质。为了提高低保真模型的预测能力，同时保持对控制设计的服从，我们提供了一个随机动力学建模框架，用于捕捉大气湍流对执行机构盘概念所确定的推力和发电的影响。在这种方法中，我们使用湍流速度场的随机强迫线性模型来增加解析计算的尾流速度，并在捕获功率和推力测量方面与更高保真的模型保持一致。我们的随机模型的功率谱密度通过凸优化来识别，以确保统计一致性，同时保持模型的简约性。
Low-fidelity analytical models of turbine wakes have traditionally been used to demonstrate the utility of advanced control algorithms in increasing the annual energy production of wind farms. In practice, however, it remains challenging to achieve significant performance improvements using closed-loop strategies that are based on conventional low-fidelity models. This is due to the over-simplified static nature of wake predictions from models that are agnostic to the complex aerodynamic interactions among turbines. To improve the predictive capability of low-fidelity models while remaining amenable to control design, we offer a stochastic dynamical modeling framework for capturing the effect of atmospheric turbulence on the thrust force and power generation as determined by the actuator disk concept. In this approach, we use stochastically forced linear models of the turbulent velocity field to augment the analytically computed wake velocity and achieve consistency with higher-fidelity models in capturing power and thrust force measurements. The power-spectral densities of our stochastic models are identified via convex optimization to ensure statistical consistency while preserving model parsimony.
通过激光雷达测量优化工程尾流模型
影响因子：
4
作者：
Zhan, Lu;Letizia, Stefano;Iungo, Giacomo Valerio
通讯作者：
Iungo, Giacomo Valerio
用于模拟德克萨斯州北部风电场的单向中尺度-微尺度耦合：根据 SCADA 和 LiDAR 数据进行评估
影响因子：
4.1
作者：
Santoni, Christian;García‐Cartagena, Edgardo J.;Ciri, Umberto;Zhan, Lu;Valerio Iungo, Giacomo;Leonardi, Stefano
通讯作者：
Leonardi, Stefano
风电场功率最大化的随机动态规划
DOI：
--
发表时间：
2020
期刊：
Proceedings of the American Control Conference
影响因子：
--
作者：
Guo, Yi;Rotea, Mario;Summers, Tyler
通讯作者：
Summers, Tyler