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Conference paper Open Access

Joint state-parameter estimation for a control-oriented LES wind farm model

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

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    <subfield code="a">wind farm control</subfield>
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    <subfield code="a">The Science of Making Torque from Wind</subfield>
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    <subfield code="u">Delft University of Technology</subfield>
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    <subfield code="a">Doekemeijer, Bart M</subfield>
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    <subfield code="a">Joint state-parameter estimation for a control-oriented LES wind farm model</subfield>
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    <subfield code="c">727477</subfield>
    <subfield code="a">Closed Loop Wind Farm Control</subfield>
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    <subfield code="a">&lt;p&gt;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.&lt;/p&gt;</subfield>
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