August 3, 2018 Conference paper Open Access

Doekemeijer, Bart M; Boersma, Sjoerd; Pao, Lucy Y; van Wingerden, Jan-Willem

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    "version": "final", 
    "language": "eng", 
    "title": "Joint state-parameter estimation for a control-oriented LES wind farm model", 
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      "id": "CC-BY-NC-SA-3.0"
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        "title": "Closed Loop Wind Farm Control", 
        "acronym": "CL-Windcon", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
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    "keywords": [
      "wind farm control", 
      "state estimation", 
      "closed-loop", 
      "kalman filtering"
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    "publication_date": "2018-08-03", 
    "creators": [
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        "orcid": "0000-0003-2757-1615", 
        "affiliation": "Delft University of Technology", 
        "name": "Doekemeijer, Bart M"
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      {
        "affiliation": "Delft University of Technology", 
        "name": "Boersma, Sjoerd"
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      {
        "orcid": "0000-0001-9450-8902", 
        "affiliation": "University of Colorado Boulder", 
        "name": "Pao, Lucy Y"
      }, 
      {
        "orcid": "0000-0003-3061-7442", 
        "affiliation": "Delft University of Technology", 
        "name": "van Wingerden, Jan-Willem"
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    "meeting": {
      "acronym": "TORQUE 2018", 
      "dates": "20-22 June 2018", 
      "place": "Milan, Italy", 
      "title": "The Science of Making Torque from Wind"
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    "description": "<p>Wind farm control research typically relies on computationally inexpensive, surrogate models for real-time optimization. However, due to the large time delays involved, changing atmospheric conditions and tough-to-model flow and turbine dynamics, these surrogate models need constant calibration. In this paper, a novel real-time (joint state-parameter) estimation solution for a medium-fidelity dynamical wind farm model is presented. In this work, we demonstrate the estimation of the freestream wind speed, local turbulence, and local wind field in a two-turbine wind farm using exclusively turbine power measurements. The estimator employs an Ensemble Kalman filter with a low computational cost of approximately 1.0 s per timestep on a dual-core notebook CPU. This work presents an essential building block for real-time wind farm control using computationally efficient dynamical wind farm models.</p>"
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