Articles | Volume 8, issue 6
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-947-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jun 2023
Research article |
| 07 Jun 2023
| 07 Jun 2023
Grand challenges in the digitalisation of wind energy
Andrew Clifton
CORRESPONDING AUTHOR
Stuttgart Wind Energy at the Institute of Aircraft Design, University of Stuttgart, Stuttgart, Germany
now at: enviConnect, TTI GmbH, 70569 Stuttgart, Germany
Sarah Barber
Institute for Energy Technology, Eastern Switzerland University of Applied Sciences, Oberseestrasse 10, 8640 Rapperswil, Switzerland
Andrew Bray
MXV Ventures, Oakland, California, USA
now at: Aurora Energy Research, Oakland, California, USA
Peter Enevoldsen
Centre for Energy Technologies, Aarhus University, Aarhus, Denmark
Jason Fields
National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA
Anna Maria Sempreviva
Department of Wind Energy, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Lindy Williams
Computational Sciences Center, National Renewable Energy Laboratory, Golden, CO, USA
Julian Quick
The Paul M. Rady Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA
now at: Department of Wind Energy and Energy Systems, DTU, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
Mike Purdue
NRG Sytems, Hinesburg, VT, USA
Philip Totaro
IntelStor LLC, Houston, TX, USA
Yu Ding
Department of Industrial and Systems Engineering, Texas A & M University, College Station, TX, USA
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Total article views: 2,527 (including HTML, PDF, and XML)
Thereof 2,437 with geography defined
and 90 with unknown origin.
Total article views: 3,115 (including HTML, PDF, and XML)
Thereof 3,005 with geography defined
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Cited
9 citations as recorded by crossref.
- Knowledge engineering for wind energy Y. Marykovskiy et al. 10.5194/wes-9-883-2024
- Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning L. Jenkel et al. 10.3390/en16176377
- Digitalization in the Renewable Energy Sector M. El Zein & G. Gebresenbet 10.3390/en17091985
- Improving data sharing in practice – power curve benchmarking case study S. Barber & Y. Ding 10.1088/1742-6596/2745/1/012002
- Tackling grand challenges in wind energy through a socio-technical perspective J. Kirkegaard et al. 10.1038/s41560-023-01266-z
- Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study S. Barber et al. 10.3390/en15155638
- Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements Y. Ding et al. 10.3389/fenrg.2022.1050342
- Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation S. Barber et al. 10.3390/en16083567
- Data-driven virtual sensor for online loads estimation of drivetrain of wind turbines O. Kamel et al. 10.1007/s10010-023-00615-4
4 citations as recorded by crossref.
- Knowledge engineering for wind energy Y. Marykovskiy et al. 10.5194/wes-9-883-2024
- Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning L. Jenkel et al. 10.3390/en16176377
- Digitalization in the Renewable Energy Sector M. El Zein & G. Gebresenbet 10.3390/en17091985
- Improving data sharing in practice – power curve benchmarking case study S. Barber & Y. Ding 10.1088/1742-6596/2745/1/012002
5 citations as recorded by crossref.
- Tackling grand challenges in wind energy through a socio-technical perspective J. Kirkegaard et al. 10.1038/s41560-023-01266-z
- Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study S. Barber et al. 10.3390/en15155638
- Data-Driven wind turbine performance assessment and quantification using SCADA data and field measurements Y. Ding et al. 10.3389/fenrg.2022.1050342
- Best Practice Data Sharing Guidelines for Wind Turbine Fault Detection Model Evaluation S. Barber et al. 10.3390/en16083567
- Data-driven virtual sensor for online loads estimation of drivetrain of wind turbines O. Kamel et al. 10.1007/s10010-023-00615-4
Latest update: 11 May 2024
Short summary
Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Wind energy creates huge amounts of data, which can be used to improve plant design, raise...
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