Estimation of the Ambient Wind Field From Wind Turbine Measurements Using Gaussian Process Regression

Conference · Wed Jul 28 04:00:00 EDT 2021

DOI:https://doi.org/10.23919/ACC50511.2021.9483088· OSTI ID:1818221

van der Hoek, Daan; Sinner, Michael; Simley, Eric; Pao, Lucy; van Wingerden, Jan-Willem

In the search for a lower levelized cost of wind energy, one approach is to increase the accuracy of wind turbine measurements such as wind speed and wind direction. The sensors available on wind turbines are susceptible to local turbulence and measurement bias, which can result in suboptimal turbine performance. As an alternative, recent research has considered using the sensor measurements in a coordinated manner. With such a cooperative approach, the local wind conditions can be estimated more accurately and reliably without the need for additional measurement equipment. In this paper, a novel wind field estimation approach is presented that estimates the local wind conditions based on turbine measurements using Gaussian processes. We show that the estimation framework is able to improve the accuracy of the wind direction estimate both in an offline and online manner, as well as identify possible biases in the sensors and reduce unnecessary wind turbine yaw activity.

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind Energy Technologies Office (EE-4W)
• DOE Contract Number: AC36-08GO28308
• OSTI ID: 1818221
• Report Number(s): NREL/CP-5000-79243; MainId:33469; UUID:262fb59d-e9c3-42bd-8838-fd916e43e6e9; MainAdminID:60638
• Country of Publication: United States
• Language: English

Related Subjects

49 EE - Wind and Water Power Program - Wind (EE-4W)
Gaussian process
estimation
wind