Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique

Wang, Longyan; Xie, Junhang; Luo, Wei; Wang, Zilu; Zhang, Bowen; Chen, Meng; Tan, Andy

doi:10.1016/j.seta.2022.102499

Cited by 4 publications

(4 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the data generated by the AS model were employed by Yang [140] to train a neural network model to predict the instantaneous wake centers. The data-driven wind turbine wake model of Wang et al [141] was developed based on a database generated with the actuator disk model with rotation (AD-R). The reduced-order model developed by Iungo et al [142] to predict the instantaneous flow field partially utilized the data from the AL simulations.…”

Section: Discussionmentioning

confidence: 99%

Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes

Liu

Yang

2022

Energies

View full text Add to dashboard Cite

Wind turbine parameterization models, which are often employed to avoid the computational cost of resolving the blade aerodynamics, are critical for the capability of large-eddy simulation in predicting wind turbine wakes. In this paper, we review the existing wind turbine parameterization models, i.e., the actuator disk model, the actuator line model, and the actuator surface model, by presenting the fundamental concepts, some advanced issues (i.e., the force distribution approaches, the method for velocity sampling, and the tip loss correction), and their applications to utility-scale wind farms. Emphasis is placed on the predictive capability of different parameterizations for different wake characteristics, such as the blade load, the tip vortices and hub vortex in the near wake, and the meandering of the far wake. The literature demonstrated the importance of taking into account the effects of nacelle and tower in wind turbine wake predictions. The predictive capability of the actuator disk model with different model complexities, which is preferred in wind farm simulations, is systematically reviewed for different inflows and different wind turbine designs. Applications to wind farms show good agreements between simulation results and measurements.

show abstract

Section: Discussionmentioning

confidence: 99%

Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes

Liu

Yang

2022

Energies

View full text Add to dashboard Cite

show abstract

“…Wake modeling [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], in this context, involves 1) a farm-scale interaction between multiple wind turbines, where the aerodynamic wake of one turbine influences the performance of downwind turbines, and 2) a turbine-scale investigation of how the flow field of wind evolves after its interaction with a turbine. This is an interdisciplinary topic which touches on aerodynamics such as fluidstructure interactions and turbulent flow analysis.…”

Section: Wake Modelingmentioning

confidence: 99%

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Roetzer,

Li,

Hall

2024

Preprint

View full text Add to dashboard Cite

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

show abstract

“…28 The experimental velocity results from Mikkelsen’s work are used as a reference. 29 The ANN wake model is built on the dataset of wake simulation by the actuator disk (AD) method, which takes the inflow speed, tip speed ratio, and pitch angle as inputs and the predicted flow field of a single wind turbine as output. The ANN wake model is built based on the Pytorch framework and its architecture is shown in Figure 1.…”

Section: Modelingmentioning

confidence: 99%

Study of cooperative wake control for multiple wind turbines under variable wind speeds/directions

Zhang,

Xu,

Luo

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part A:

Self Cite

View full text Add to dashboard Cite

In the wind farm control field, wind turbines are normally manipulated to maximize the individual power production which is named the greedy control. However, this greedy control method can lead to massive losses of total wind farm power production, mainly caused by the wake interference between multiple wind turbines. To this end, the cooperative wake control, which seeks the maximum total power production by coordinating each individual wind turbine at the global optimum operation point, can greatly improve the wind farm output performance. In this paper, we investigate the effectiveness of two different cooperative wake control strategies, i.e., instantaneous control and wind-interval based (WIB) control under variable wind speeds/directions scenario. These two cooperative control strategies are achieved based on the power de-rating operation to the upstream wind turbines. Taking three in-line wind turbines as an example, the control parameters of the two upstream wind turbines are cooperatively optimized while the downstream third wind turbine operates at the maximum power coefficient. To account for the multiple wind turbines wake interference, an artificial neural network (ANN) wake model characterized by the fast computational efficiency and great accuracy, in combination with the best wake superposition model chosen to quantify multiple wake effect, is proposed for the control optimization. By comparing to the baseline greedy control, it shows that both cooperative control strategies are effective in improving the power production of the wind farm. More specifically, the WIB control can maintain the power production at the same level of instantaneous control with a maximum difference less than 3%, while it reduces the operating difficulty to a large extent which greatly facilitates its application under realistic more complex wind scenarios.

show abstract

Effectiveness of data-driven wind turbine wake models developed by machine/deep learning with spatial-segmentation technique

Cited by 4 publications

References 24 publications

Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes

Review of Turbine Parameterization Models for Large-Eddy Simulation of Wind Turbine Wakes

Review of Data-Driven Models in Wind Energy: Demonstration of Blade Twist Optimization Based on Aerodynamic Loads

Study of cooperative wake control for multiple wind turbines under variable wind speeds/directions

Contact Info

Product

Resources

About