时变入流下风电场控制的随机序贯反馈优化
Stochastic Sequential Feedback Optimization for Wind Farm Control under Time-Varying Inflow
浏览论文内容
中文总结 AI 辅助
针对时变随机运行条件下的非线性系统稳态优化问题，提出随机序贯反馈优化（S2FO）方法，结合投影反馈梯度与序贯灵敏度估计，并应用于风电场功率最大化，仿真显示其在持续尾流下提升平均发电功率。
中文摘要 AI 辅助
我们考虑在时变随机运行条件下非线性动力系统的稳态优化问题，其中目标函数是当前运行条件分布上的期望值函数，因此分布的变化会引发移动的随机最优值。为解决该问题，我们提出了随机序贯反馈优化（S2FO），该方法将投影反馈梯度更新与通过在实现的随机运行条件下对设备进行线性化获得的序贯灵敏度估计相结合。我们通过期望动态遗憾、期望中的瞬时跟踪以及递减步长下的几乎必然收敛性来分析S2FO的闭环性能。随后，我们将该方法应用于时变随机入流下的风电场功率最大化。在基准布局和实际海上风电场的仿真表明，当存在持续尾流相互作用时，S2FO提高了平均发电功率，并且其优势随着风向变率的增加而减小。
英文摘要
We consider steady-state optimization of nonlinear dynamical systems under time-varying stochastic operating conditions, where the objective is an expected-value function over the current operating-condition distribution, so that changes in the distribution induce a moving stochastic optimum. To address this problem, we propose stochastic sequential feedback optimization (S2FO), which combines projected feedback-gradient updates with sequential sensitivity estimates obtained by linearizing the plant at realized stochastic operating conditions. We analyze the closed-loop performance of S2FO through expected dynamic regret, instantaneous tracking in expectation, and almost-sure convergence under diminishing step sizes. We then apply the method to wind farm power maximization under time-varying stochastic inflow. Simulations on a benchmark layout and on a realistic offshore wind farm show that S2FO improves average power generation when persistent wake interactions are present, and that its advantage decreases as wind-direction variability increases.
发表机构
- Delft Center of Systems and Control, TU Delft（代尔夫特系统与控制中心，代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
