Prediction of wind farm wake and output power using generative adversarial network and convolutional neural network

Chen, Jian, Yu, Chongyang, Xu, Zhongyun, He, Ruiyang ORCID: https://orcid.org/0000-0002-9643-9485, Li, Chun, Zhang, Wanfu and Wang, Ying 2025. Prediction of wind farm wake and output power using generative adversarial network and convolutional neural network. Physics of Fluids 37 (8) , 085255. 10.1063/5.0284856

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Abstract

Wake and output power prediction of wind turbine is critical for the wind farm layout optimization. Previous studies used analytical wake models and computational fluid dynamics (CFD) methods to fulfill this prediction. However, these methods either exhibit inadequate prediction accuracy or need excessive computational demands during prediction processes. Thus, a novel wind farm prediction system is established using the full-model CFD simulation and a surrogate modeling method based on convolutional neural networks and generative adversarial networks to ensure the fidelity and efficiency of the prediction. By containing an incoming speed distribution generator module (Gin), a wake distribution generator model (Gw), a rotational speed prediction module (R), and a power prediction module (P), this system can predict high-dimensional incoming data, the rotational speed, power output, and three-dimensional wake fields. The system uses the incoming wind speed (Vin) and turbulence intensity to determine the optimal placement of turbines in the wind field. The Gin, R, Gw, and P module are validated through high-resolution experimental and computational data. The system is applied to a tandem wind farm and the Horns Rev 1 wind farm. The predicted data show satisfactory agreement with high-resolution experimental and computational data, fully validating the robustness and generalization capability of the system. The proposed prediction system is of great engineering significance for optimizing wind farm layout.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	American Institute of Physics
ISSN:	1070-6631
Date of First Compliant Deposit:	16 September 2025
Date of Acceptance:	5 August 2025
Last Modified:	16 Sep 2025 15:30
URI:	https://orca.cardiff.ac.uk/id/eprint/180989

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