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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2020
عنوان انگلیسی مقاله:
Wake modeling of wind turbines using machine learning
ترجمه فارسی عنوان مقاله:
مدل سازی توربین های بادی با استفاده از یادگیری ماشین
منبع:
Sciencedirect - Elsevier - Applied Energy, 257 (2020) 114025: doi:10:1016/j:apenergy:2019:114025
نویسنده:
Zilong Tia, Xiao Wei Denga,⁎, Hongxing Yangb
چکیده انگلیسی:
In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics)
simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is
proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation
(BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions
and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model
ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes
equations) simulations coupled with a modified k − ε turbulence model to provide big datasets of wake flow for
training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is
validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines.
In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as
input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are
taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the
spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev
wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased
wake model show good agreement with the numerical simulations and measurement data, indicating that
the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake
flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions.
Keywords: Wind turbine wake | Wake model | Artificial neural network (ANN) | Machine learning | ADM-R (actuator-disk model with rotation) | model | Computational fluid dynamics (CFD)
قیمت: رایگان
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