A multi-fidelity framework for power prediction of wind farm under yaw misalignment

Yu Tu, Yaoran Chen, Kai Zhang, Ruiyang He, Zhaolong Han and Dai Zhou

Applied Energy, 2025, vol. 377, issue PC, No S0306261924019834

Abstract: Collective yaw control is a promising approach for wind farm flow control. The investigation approaches exhibit varying levels of fidelity. High-fidelity numerical simulations offer accurate representations but are computationally expensive, while low-fidelity analytical models provide rapid calculations with reduced accuracy. Confronted with this dilemma, the multi-fidelity surrogate model emerges as a compelling solution. In this paper, we introduce a multi-fidelity framework based on the co-Kriging algorithm to efficiently predict the wind farm power under yaw misalignment. Two surrogate models are compared, the single-fidelity Kriging (SFK) model and the multi-fidelity co-Kriging (MFK) model. Both models provide desired accuracy, with MFK model outperforming SFK model. For one-dimensional and three-dimensional cases, the MFK model significantly reduces the demanding high-fidelity samples, while remains accuracy of 96.5% and 93.9%, respectively. We further investigate the influence of low-fidelity data sources on MFK model, including the Gauss-Curl Hybrid (GCH) wake model, Gaussian wake model, and Jensen wake model. The MFK-GCH model shows better prediction accuracy, indicating that low-fidelity data with more physical information benefits the model. Furthermore, sensitivity analysis is given to ensure reliable and consistent results. The multi-fidelity framework enables efficient and accurate power prediction, which lays the foundation of yaw optimization in large-scale wind farms.

Keywords: Yaw control; Co-Kriging; Surrogate model; Multi-fidelity; Large eddy simulation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124600

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