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. 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 epsilon 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. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A novel machine-learning-based wake model is developed in this study to improve the power prediction accuracy of wind farms, with high computational efficiency. The model establishes the implicit relationship between inflows and wake flows using Artificial Neural Networks based on massive CFD simulation dataset. The validated model shows significant improvements in power predictions compared to existing analytical models, matching well with LES and measurement data.