论文标题
基于DFIG的风电场的数据驱动模型预测控制方法提供一级频率调节服务
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service
论文作者
论文摘要
随着风力渗透的增加，新释放的网格代码需要风电场以提供频率调节服务。最关键的挑战是如何制定风电场的动态模型进行动态控制，因为它本质上是非线性的，并且有大量参数需要经常维持。本文提出了一种数据驱动的模型预测控制（数据驱动的MPC）方法，以使风电场参与初级频率调节。在这种方法中，开发了一种专业的动态模式分解（SDMD）算法，该算法可以线性地近似于基于Koopman Operator Theory的测量，从基于Koopman Operator Theory.com等的测量中的动态近似于现有的扩展动态模式分解（EDMD）方法，该方法是在良好的捕获型号的良好型动态的良好型动力学下，这是两个量身定制的sdmd均可启发的sdmd。 2）降低模型维度的计算负担要小得多。基于递归更新的线性动态模型，实现了模型预测控制解决方案。仿真结果表明，这种无模型解决方案可以动态优化风力涡轮机发电机的主动功率，以跟踪系统操作员的频率响应要求并最大程度地减少转子速度失真。
As wind power penetration increases, the wind farms are required by newly released grid codes to provide frequency regulation service. The most critical challenge is how to formulate the dynamic model of wind farm for dynamic control, since it is essentially is nonlinear and there are huge amount of parameters to be maintained frequently. This paper proposes a data-driven model predictive control (data-driven MPC) method to make wind farms participate primary frequency regulation. In this method,a specialized dynamic mode decomposition (SDMD) algorithm is developed, which can linearly approximate the dynamics of wind farm from measurements based on Koopman operator theory.Compared with the existing extended dynamic mode decomposition (EDMD) method,this tailored SDMD has two advantages: 1) fully capturing the nonlinear transients of wind turbine dynamics with good accuracy under a wide range of working conditions; 2) much less computational burden with model dimensionality reduction. Based on the recursively updated linear dynamic model, a model predictive control solution is implemented. The simulation results show this model-free solution can dynamically optimize wind turbine generators' active power to track the frequency response requirement from system operator and minimize the rotor speed distortion.
