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In the wind industry, power prediction of wind farm is commonly implemented by analytical wake models, which is low-cost but insufficient in accuracy for high-turbulent wake modelling. In this study, a novel machine-learning-based wake model is developed to improve the power prediction of wind farms. The presented model can reproduce the velocity and turbulence fields in turbine wakes commensurate to the high-fidelity Computational Fluid Dynamics (CFD) simulations while achieving good computational efficiency. Driven by massive CFD simulation dataset, the implicit relationship between inflows and wake flows is established using the Artificial Neural Networks (ANN) technique based on back-propagation algorithm. The reduced-order method Actuator Disk Model with Rotation (ADM-R) and modified k − ε turbulence model are implemented into RANS simulations to save the computational costs dramatically in producing the big-data of wake flows. The ANN wake model is deployed in the Horn Rev wind farm, and validated against LES, onsite measurement, and analytical wake models. The conclusions show that the ANN model can appreciably improve the power predictions compared with the existing analytical models and match the LES and measurement data well. The validated model is also adopted to investigate the influence of wind direction and turbine layout on power production of wind farms. • The machine learning-based wake model is competitive in both efficiency and accuracy for wind farm power prediction. • Turbulence modeling dominates the recovery speed of turbine wake, which is critical for accurate power prediction. • Comprehensive predictions of power output and losses inside Horns Rev wind farm under various wind directions are discussed. • In the Horns Rev wind farm, aligned layout performs better than staggered layout due to the local dominated wind directions. [ABSTRACT FROM AUTHOR]

This paper presents a data-driven wind farm layout optimization framework that uses a machine learning wake model that considers physical control stages. The machine learning wake model is trained using well-validated Computational Fluid Dynamics (CFD) simulation data, and consists of thousands of sub-models, each of which is an artificial neural network (ANN) wake model. The ANN wake models are trained separately for low-speed and high-speed inflows to ensure high accuracy of the predictions, with less than 2% error compared to CFD simulations. The accuracy and efficiency of the framework are validated, and the results show better agreement with CFD simulation than an analytical wake model developed in recent years. A parametric study on the number of wind turbines in the Horns Rev wind farm demonstrates that more wind turbines can be added with a minor decrease in average power, with more even and staggered layouts. Under full-wake uniform inflow, the selected analytical wake model exhibits a power prediction error of 5%–8%, while the differences between optimal layouts searched by different wake models range from 2% to 8%. When introducing a wider range of inflow direction sectors, the discrepancy between optimal layout performances decreases. [ABSTRACT FROM AUTHOR]

A numerical wake model that is capable of simulating wind turbine wake effects over complex terrains is proposed in this work. Currently, full computational fluid dynamics (CFD) simulations are required to simulate wake effects over complex terrains. Due to their high computational cost, it is difficult to apply expensive CFD simulations in the wind farm design process as optimization algorithms often require large number of solution evaluations. The proposed wake model solves a simplified variation of the Navier-Stokes equations, in which simplifications and assumptions have been implemented in order to reduce computational cost while maintaining accuracy. This model was validated by comparing with full CFD simulations with reasonable accuracy. In general, the model produces accurate results while keeping the computational cost two orders of magnitude lower than that of full CFD simulations. The low computation time highlights the model's potential to be used for optimizing wind farm layouts on complex terrains. [ABSTRACT FROM AUTHOR]

• 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. 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. [ABSTRACT FROM AUTHOR]