Mobile Sensing for Wind Field Estimation in Wind Farms: Preprint

David Pasley, Marco Nicotra, Lucy Pao, Jennifer King, Christopher Bay

Research output: Contribution to conferencePaper


This paper introduces a novel approach for estimating the wind field over an entire wind farm using a mobile sensor to collect limited amounts of data. The proposed method estimates the boundary conditions of a simplified turbine wake model by computing the model sensitivity matrix and using a recursive least-squares algorithm to recover the model parameters from the wind field measurements. To address the fact that it is not practical to take measurements across the entire wind farm, the proposed method classifies each area on the map based on its sensitivity to parameter variations. This classification is then used to generate a suitable path for a mobile sensor, which is charged with collecting data for the recursive least-squares algorithm. The proposed framework can successfully estimate the model boundary conditions using just the measurements collected along the path of the mobile sensor. This preliminary result paves the way for using real-time wind field estimates for the coordinated control of all the turbines within a wind farm.
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-5000-77743 for paper as published in proceeding

NREL Publication Number

  • NREL/CP-5000-76133


  • estimation
  • mobile sensing
  • wind farm control


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