Online Learning of Effective Turbine Wind Speed in Wind Farms: Preprint

Conference · Wed Jan 17 04:00:00 EST 2024

DOI:https://doi.org/10.1109/CDC49753.2023.10383909· OSTI ID:2282545

Henry, Aoife; Sinner, Michael; King, Jennifer; Pao, Lucy Y.

To develop better wind farm controllers that can meet more complex objectives, methods of modeling the wind turbine wakes at low computational expense are needed. Gaussian process (GP) regression offers a computationally inexpensive framework for learning complex functions from noisy measurements with very few datapoints. In this work, an online learning approach is presented to learn the rotor- averaged wind velocity at downstream wind turbines with GPs, using the available datastream of wind field measurements and wind turbine control set-points. This framework can readily be integrated into model-based controls methods because the model a) is updated online at low computational expense, b) assumes a mathematically favorable Gaussian form, and c) explicitly quantifies the stochastic nature of the wake field so that the trade-off between exploration and exploitation, and the uncertainty in the prediction, can be utilized. We show that a GP-learned model can match true values with errors within 0.5% on average, with as few as 5 training data points.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 2282545
• Report Number(s): NREL/CP-5000-87155; MainId:87930; UUID:a5b09c08-4064-4e3f-b0e6-e4727a7b38f2; MainAdminID:71568
• Country of Publication: United States
• Language: English

Related Subjects

Gaussian process
WIND ENERGY
machine learning
surrogate model
wake model