论文标题
使用高斯流程回归和高保真模拟数据改编工程唤醒模型
Adaptation of Engineering Wake Models using Gaussian Process Regression and High-Fidelity Simulation Data
论文作者
论文摘要
本文调查了九个涡轮风电场的偏航控制输入的优化。使用高保真模拟器Sowfa模拟了风电场。通过基于高斯过程的修饰符适应方案进行优化。修饰符的适应性纠正了植物和模型之间的不匹配，并有助于收敛到实际计划最佳。在案例研究中，将修饰符适应方法与贝叶斯优化方法进行了比较。此外，讨论了在高斯过程回归中使用两个不同的协方差函数。给出了有关该方法的数据准备和应用的实用建议。结果表明，与高斯唤醒模型相比，修饰符的适应和贝叶斯优化方法都可以通过总体较小的偏航不对对准来改善功率产生。
This article investigates the optimization of yaw control inputs of a nine-turbine wind farm. The wind farm is simulated using the high-fidelity simulator SOWFA. The optimization is performed with a modifier adaptation scheme based on Gaussian processes. Modifier adaptation corrects for the mismatch between plant and model and helps to converge to the actual plan optimum. In the case study the modifier adaptation approach is compared with the Bayesian optimization approach. Moreover, the use of two different covariance functions in the Gaussian process regression is discussed. Practical recommendations concerning the data preparation and application of the approach are given. It is shown that both the modifier adaptation and the Bayesian optimization approach can improve the power production with overall smaller yaw misalignments in comparison to the Gaussian wake model.
