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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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Doekemeijer, Bart M</dc:creator>
  <dc:creator>Boersma, Sjoerd</dc:creator>
  <dc:creator>Pao, Lucy Y</dc:creator>
  <dc:creator>van Wingerden, Jan-Willem</dc:creator>
  <dc:description>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.</dc:description>
  <dc:subject>wind farm control</dc:subject>
  <dc:subject>state estimation</dc:subject>
  <dc:subject>kalman filtering</dc:subject>
  <dc:title>Joint state-parameter estimation for a control-oriented LES wind farm model</dc:title>
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