Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 1
Articles | Volume 6, issue 1
https://doi.org/10.5194/wes-6-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 1
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Model-free estimation of available power using deep learning

Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio

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Annoni, J., Taylor, T., Bay, C., Johnson, K., Pao, L., Fleming, P., and Dykes, K.: Sparse-Sensor Placement for Wind Farm Control, J. Phys.: Conf. Ser., 1037, 032019, https://doi.org/10.1088/1742-6596/1037/3/032019, 2018. a
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Bak, C., Bitsche, R., Yde, A., Kim, T., Hansen, M. H., Zahle, F., Gaunaa, M., Blasques, J., Døssing, M., Heinen, J. J. W., and Behrens, T.: Light rotor: The 10-MW Reference Wind Turbine, in: European Wind Energy Conference and Exhibition 2012, EWEC 2012, 16–19 April 2012, Copenhagen, Denmark, 2012. a, b
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Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
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