Development of a surrogate model for wind farm control
Development of a surrogate model for wind farm control
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风电场控制替代模型的开发
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
S. Leonardi
中科院分区：
文献类型：
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作者：
U. Ciri;C. Santoni;F. Bernardoni;M. Salvetti;S. Leonardi
我们提出了一种方法来获得一个代理模型的风电场控制。该过程是基于一个随机的方法，使用广义多项式混沌（PC）和高保真度模拟。涡轮机控制律和来风条件，如速度和方向，被视为不确定变量。将风电场的发电量视为依赖于这些不确定变量的随机过程。因此，使用多项式混沌展开来获得响应函数，该响应函数提供作为涡轮机控制参数以及风速和风向的函数的风电场功率生产。响应函数是通过使用一组有限的确定性实现，其中包括在高保真模拟的风速，方向和控制参数的某些值，插值多项式。在PC中，根据不确定参数的概率密度函数选择插值多项式基和实现集。这允许使用有限数量的实现来获得准确的响应函数，并提供模型的不确定性界限。因此，针对任何风速和风向获得最优控制设置的映射，以用于实时风电场操作。在这项工作中，该程序进行了验证，对现场测量在德克萨斯州北部的一个真实的风电场。代理模型是通过使用我们的内部代码进行64次模拟来获得的，该代码由7阶Hermite多项式插值。用代理模型计算的发电量在测量的SCADA数据的2%以内是准确的。一旦获得响应函数，就解决优化问题以找到使风电场功率生产最大化的控制参数。
We present a method to derive a surrogate model for wind farm control. The procedure is based on a stochastic approach using generalized polynomial chaos (PC) and high-fidelity simulations. The turbine control law and the incoming wind conditions, such as speed and directions, are treated as uncertain variables. Wind farm power production is viewed as the random process depending on these uncertain variables. Thus, polynomial chaos expansion is used to obtain a response function that provides the wind farm power production as a function of the turbine control parameters and the wind speed and direction. The response function is obtained by using a finite set of deterministic realizations, which consist in high-fidelity simulations for certain values of wind speed, direction and control parameters, interpolated by polynomials. In PC, the interpolating polynomial basis and the set of realizations are selected according to the probability density function of the uncertain parameters. This allows using a limited number of realizations to obtain an accurate response function and provides uncertainty bounds on the model. Thus, a mapping of the optimal control settings is obtained for any wind speed and direction to be employed for real-time wind farm operations. In this work, the procedure is validated against field measurements in a real wind farm in north Texas. The surrogate model is obtained by performing 64 simulations with our in-house code interpolated by 7th -order Hermite polynomials. The energy production computed with the surrogate model is accurate within 2% of the measured SCADA data. Once the response function has been obtained, an optimization problem is solved to find the control parameters maximizing the wind farm power production.