Predicting Faults in Wind Turbines Using SCADA Data

Anders Bech Borchersen, Jesper Abildgaard Larsen, Jakob Stoustrup

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceabstrakt i proceedingForskningpeer review

2 Citationer (Scopus)

Resumé

The cost of operation and maintenance of wind turbines is a significant part of the overall cost of wind turbines. To reduce this cost a method for enabling early fault detection is proposed and tested in this paper. The method is taking advantage of the fact that wind turbines in wind farms are located near similar wind turbines. This is done by generating a model for each turbine, the model is then used to evaluate the performance of that turbine and the nearby turbines. The evaluations from the models are then combined and used as votes to identify the faulty turbines. The method is applied and tested on historical Supervisory Control And Data Acquisition (SCADA) data from nine operational turbines over a testing period of nine months. The performance of the fault detection is found to be acceptable based on the testing period. During the testing period several gear related services were performed, some of these were predicted by the proposed fault detection systems. The advantage of the purposed method is that it applicable for operational turbines without requiring any extra measurements, since the used SCADA data is available from most modern wind turbines.
OriginalsprogEngelsk
Titel51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
ForlagAmerican Institute of Aeronautics and Astronautics
Publikationsdato7 jan. 2013
ISBN (Trykt)978-1-62410-181-6
DOI
StatusUdgivet - 7 jan. 2013
Begivenhed31st ASME Wind Energy Symposium - Grapevine, TX, USA
Varighed: 7 jan. 201310 jan. 2013

Konference

Konference31st ASME Wind Energy Symposium
LandUSA
ByGrapevine, TX
Periode07/01/201310/01/2013

Fingerprint

Wind turbines
Data acquisition
Turbines
Fault detection
Testing
Costs
Farms
Gears

Citer dette

Borchersen, A. B., Larsen, J. A., & Stoustrup, J. (2013). Predicting Faults in Wind Turbines Using SCADA Data. I 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2013-313
Borchersen, Anders Bech ; Larsen, Jesper Abildgaard ; Stoustrup, Jakob. / Predicting Faults in Wind Turbines Using SCADA Data. 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics, 2013.
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Borchersen, AB, Larsen, JA & Stoustrup, J 2013, Predicting Faults in Wind Turbines Using SCADA Data. i 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics, 31st ASME Wind Energy Symposium, Grapevine, TX, USA, 07/01/2013. https://doi.org/10.2514/6.2013-313

Predicting Faults in Wind Turbines Using SCADA Data. / Borchersen, Anders Bech; Larsen, Jesper Abildgaard; Stoustrup, Jakob.

51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics, 2013.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceabstrakt i proceedingForskningpeer review

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AB - The cost of operation and maintenance of wind turbines is a significant part of the overall cost of wind turbines. To reduce this cost a method for enabling early fault detection is proposed and tested in this paper. The method is taking advantage of the fact that wind turbines in wind farms are located near similar wind turbines. This is done by generating a model for each turbine, the model is then used to evaluate the performance of that turbine and the nearby turbines. The evaluations from the models are then combined and used as votes to identify the faulty turbines. The method is applied and tested on historical Supervisory Control And Data Acquisition (SCADA) data from nine operational turbines over a testing period of nine months. The performance of the fault detection is found to be acceptable based on the testing period. During the testing period several gear related services were performed, some of these were predicted by the proposed fault detection systems. The advantage of the purposed method is that it applicable for operational turbines without requiring any extra measurements, since the used SCADA data is available from most modern wind turbines.

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Borchersen AB, Larsen JA, Stoustrup J. Predicting Faults in Wind Turbines Using SCADA Data. I 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. American Institute of Aeronautics and Astronautics. 2013 https://doi.org/10.2514/6.2013-313