August 3, 2018 Conference paper Open Access

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

DCAT Export

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        <foaf:name>Doekemeijer, Bart M</foaf:name>
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        <foaf:name>van Wingerden, Jan-Willem</foaf:name>
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    <dct:title>Joint state-parameter estimation for a control-oriented LES wind farm model</dct:title>
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    <dcat:keyword>wind farm control</dcat:keyword>
    <dcat:keyword>state estimation</dcat:keyword>
    <dcat:keyword>closed-loop</dcat:keyword>
    <dcat:keyword>kalman filtering</dcat:keyword>
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    <dct:description>&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;</dct:description>
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