|Wind farm operation and maintenance optimization using big data|Helsen, J.; Peeters, C.; Doro, P.; Ververs, E.; Jordaens, P.J. (2017). Wind farm operation and maintenance optimization using big data, in: IEEE BigDataService 2017. The Third IEEE International Conference on Big Data Computing Service and Applications, San Francisco, 6 – 10 April 2017. pp. 179-184. https://hdl.handle.net/10.1109/BigDataService.2017.27
In: (2017). IEEE BigDataService 2017. The Third IEEE International Conference on Big Data Computing Service and Applications, San Francisco, 6 – 10 April 2017. IEEE: [s.l.]. ISBN 978-1-5090-6318-5. xxv, 322 pp.
wind turbine; wind energy; prognostics; failure; vibrations; offshorewind
|Auteurs|| || Top |
- Helsen, J.
- Peeters, C.
- Doro, P.
- Ververs, E.
- Jordaens, P.J.
In the current electricity production mix wind energy is claiming a significant part. In order to guarantee stable electricity production predictability of the wind farm operational behaviour is essential. Big data approaches have the potential for a significant role in realizing this goal. In order to gain insights in turbine operational behaviour it is necessary to obtain a farm wide dataset, containing the operational sensor data of the different machines and context information such as maintenance data. Advanced analytics can use this data for understanding normal and deviating turbine operational behaviour. These insights will help in optimizing the operation and maintenance strategy of the farm. This paper gives an overview of our big data approach for data-storage and illustrates some of our data-analytics research tracks for gaining insights in the underlying failure mechanisms of turbines.