Estimation of Large-Scale Wind Field Characteristics Using Supervisory Control and Data: Preprint

Research output: Contribution to conferencePaper


As the wind energy industry continues to pushfor increased power production and lower cost of energy, thefocus of research has expanded from individual turbines toentire wind farms. Among a host of interesting problems to besolved when considering the wind farm as a whole, we considerthe challenge of scalar field estimation, based on informationalready collected at the individual turbine level. We aim toestimate the large-scale, low-frequency characteristics of thewind field, such as the mean wind direction and the overalldecrease in wind speed across the farm, and employ a Kalmanfilter that models the wind field using a polynomial function.We compare the proposed method’s performance to both asimple averaging technique and filtering of individual turbinemeasurements. The method presented is not limited to windturbines and is applicable in other situations where multipleremote agents are used to estimate a scalar field.
Original languageAmerican English
Number of pages9
StatePublished - 2020
Event2020 American Control Conference (ACC) -
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference (ACC)

Bibliographical note

See NREL/CP-5000-77745 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-76003


  • large scale
  • supervisory control and data acquisition
  • turbine control
  • wind energy
  • wind field


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