Artificial Neural Networks based wake model for power prediction of wind farm

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...

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Veröffentlicht in:Renewable energy Jg. 172; S. 618 - 631
Hauptverfasser: Ti, Zilong, Deng, Xiao Wei, Zhang, Mingming
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.07.2021
Schlagworte:
algorithms
Artificial neural networks
Computational fluid dynamics
data collection
Machine learning
model validation
neural networks
power generation
Power prediction
prediction
turbulent flow
Wake model
wind direction
Wind farm
wind farms
Wake model
Power prediction
Computational fluid dynamics
Machine learning
Artificial neural networks
Wind farm
ISSN:0960-1481, 1879-0682
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Zusammenfassung: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.
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2021.03.030