Wake modeling of wind turbines using machine learning. (1st January 2020)

• Record Type: Journal Article
• Authors: Ti, Zilong; Deng, Xiao Wei; Yang, Hongxing
• Is Part Of: Applied energy. Volume 257(2020)
• Publication Date: 2020-01-01
• Subjects: Wind turbine wake -- Wake model -- Artificial neural network (ANN) -- Machine learning -- ADM-R (actuator-disk model with rotation) model -- Computational fluid dynamics (CFD); Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.114025
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: The machine learning framework achieves favorable accuracy and efficiency in the wake prediction. ADM-R/RANS coupling model can efficiently produce big wake datasets for machine learning model. For an aligned turbine row, power drops at the second turbine and gradually stabilizes afterwards. Turbulence model plays an important role in accurate power prediction of wind farm. Abstract: 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 back-propagation (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 2 MW 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 ANN-based 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.