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

Daan Van Der Hoek, Michael Sinner, Eric Simley, Lucy Pao, Jan Willem Van Wingerden

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


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.

Original languageEnglish
Title of host publicationProceedings if the American Control Conference, ACC 2021
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-6654-4197-1
Publication statusPublished - 2021
Event2021 American Control Conference, ACC 2021 - Virtual, New Orleans, United States
Duration: 25 May 202128 May 2021


Conference2021 American Control Conference, ACC 2021
CountryUnited States
CityVirtual, New Orleans


Dive into the research topics of 'Estimation of the Ambient Wind Field from Wind Turbine Measurements Using Gaussian Process Regression'. Together they form a unique fingerprint.

Cite this