Employing Bayesian Quadrature to Improve Fitting of Surrogate Models to Wind Turbine Loads. Issue 4 (1st May 2022)

• Record Type: Journal Article
• Authors: Caterina Clark, Anna; Clark, Caitlyn E.
• Is Part Of: Journal of physics. Volume 2265:Issue 4(2022)
• Publication Date: 2022-05-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2265/4/042045
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Stochastic, high-fidelity simulations are increasingly used to estimate wind turbine and plant loads and performance. While these simulations are more accurate, they are prohibitively computationally expensive. Surrogate models can be used to replace direct simulation for lower computational cost; for stochastic surfaces, a Gaussian Process (GP) is appropriate for random variables that follow Gaussian distributions, such as turbine loads and performance. In this study, we employ an advanced surrogate modeling technique to estimate the loads, reliability, and energy production of wind turbines in a wind plant, based on mid-fidelity aero-elastic simulations. Specifically, we use Bayesian Quadrature (BQ) to produce a training data set that, for a given size, minimizes the variance over the entire domain of a GP surrogate model to estimate rotor torque load on a waked turbine. When applied to a GP that was trained with aero-elastic simulation data, the BQ-enabled algorithm reduced variance—a measure of predictive uncertainty—over the entire GP to < 0.3 with 30 samples. When compared to a uniform sampling approach with the same solution space, the BQ method reduces computational time by 99.88%, or over 590 simulation evaluations.