Estimation of Large-Scale Wind Field Characteristics Using Supervisory Control and Data Acquisition Measurements

Research output: Contribution to conferencePaperpeer-review

6 Scopus Citations


As the wind energy industry continues to push for increased power production and lower cost of energy, the focus of research has expanded from individual turbines to entire wind farms. Among a host of interesting problems to be solved when considering the wind farm as a whole, we consider the challenge of scalar field estimation, based on information already collected at the individual turbine level. We aim to estimate the large-scale, low-frequency characteristics of the wind field, such as the mean wind direction and the overall decrease in wind speed across the farm, and employ a Kalman filter that models the wind field using a polynomial function. We compare the proposed method's performance to both a simple averaging technique and filtering of individual turbine measurements. The method presented is not limited to wind turbines and is applicable in other situations where multiple remote agents are used to estimate a scalar field.

Original languageAmerican English
Number of pages6
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-5000-76003 for preprint

NREL Publication Number

  • NREL/CP-5000-77745


  • estimation
  • Kalman filters
  • wind farms
  • wind speed
  • wind turbines


Dive into the research topics of 'Estimation of Large-Scale Wind Field Characteristics Using Supervisory Control and Data Acquisition Measurements'. Together they form a unique fingerprint.

Cite this