STUDY ON THREE-DIMENSIONAL STALL MODEL OF WIND TURBINE BLADES BASED ON NEURAL NETWORKS

Dai Liping; Zhang Ze'neng; Cong Longfu; Chang Ning; Zhan Peng; Wang Chao

doi:10.19912/j.0254-0096.tynxb.2023-1491

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 53-59. DOI: 10.19912/j.0254-0096.tynxb.2023-1491

STUDY ON THREE-DIMENSIONAL STALL MODEL OF WIND TURBINE BLADES BASED ON NEURAL NETWORKS

Dai Liping¹, Zhang Ze'neng¹, Cong Longfu¹, Chang Ning¹, Zhan Peng², Wang Chao²

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Abstract

In this paper the flow field of wind turbine Phase Ⅵ under various operating conditions are calculated via CFD method . The three-dimensional aerodynamic data such as attack angle, lift force and drag force are extracted using the reverse blade element momentum theory method. Based on the above data, a BP neural network correction model is established with circumferential speed ratio, solidity, attack angle, twist angle, and two-dimensional aerodynamic parameters as the input parameters and three-dimensional aerodynamic performance parameters as the output parameters. The error between the predicted lift resistance coefficient of the BP model and the result obtained from CFD calculation is within 5%. The model combining blade momentum element theory with BP neutral network is selected to calculate the aerodynamic performance of Phase Ⅵ wind turbine, and the results showed that it can predict more accurately compared with original BEM method.

Key words

wind turbines / aerodynamic stalling / rotational flow / separation / angle of attack / neural networks

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Dai Liping, Zhang Ze'neng, Cong Longfu, Chang Ning, Zhan Peng, Wang Chao. STUDY ON THREE-DIMENSIONAL STALL MODEL OF WIND TURBINE BLADES BASED ON NEURAL NETWORKS[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 53-59 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1491

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