机译:考虑动态唤醒效果的AGC风电场深学习辅助模型预测控制
State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing 100087 China;
State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing 100087 China;
State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing 100087 China;
State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing 100087 China;
State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing 100087 China Department of Electrical and Computer Engineering University of Macau Macau 999078 China;
Reduced-order model; Wind farm; Active power tracking; Dynamic wake effect; Deep learning; Model predictive control;
机译:APS-APS流体动力学分部第70届年会-事件-具有风场和转矩控制协调的动态唤醒模型,用于功率跟踪
机译:基于深度学习的新型动态风电场唤醒模型
机译:基于伴随的模型预测控制在唤醒风电场中实现最佳能量提取
机译:风电场Wake重定向模型预测控制:Koopman动态模式分解方法
机译:风电场中风轮机尾流的CFD建模。
机译:风电场动态感应控制及其与大气边界层相互作用的最优控制框架
机译:动态尾流建模和状态估计,用于改进基于模型的风电场后退水平控制
机译:海上风电场的电气和控制方面II(ERaCO II)。第1卷:风电场的动态模型