Model-free estimation of available  power using deep learning

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

doi:https://doi.org/10.5194/wes-6-111-2021

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

Data sets

DTU 10MW reference turbine HAWC2 simulations for Model-free estimation of available power with deep learning training A. Meseguer Urban https://doi.org/10.5281/zenodo.3531410

Model code and software

Deep learning for available power estimation T. Göçmen, J. Liew, and A. Meseguer Urban https://doi.org/10.5281/zenodo.3531414

Short summary

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.