access icon free Comparison of methods for wind turbine condition monitoring with SCADA data

Wind turbine operational costs can be reduced by monitoring the condition of major components in the drivetrain. SCADA-based condition monitoring is attractive because the data are already collected, resulting in rapid deployment and modest set-up cost. Three SCADA-based monitoring methods were reviewed: signal trending; self-organising maps and physical model. The physical model was identified as being the most reliable at predicting impending component failures. A validation study on this method using five operational wind farms showed that it is possible to achieve a high detection rate and good detection accuracy. An advance detection period of between 1 month and 2 years was achieved by the method. The study has also highlighted limitations and areas for further development.

Inspec keywords: maintenance engineering; SCADA systems; condition monitoring; wind turbines

Other keywords: time 1 month to 2 year; wind turbine condition monitoring; drivetrain; SCADA-based monitoring methods; physical model; self-organising maps; signal trending; wind farms

Subjects: Maintenance and reliability; Data acquisition systems; Wind power plants

References

    1. 1)
    2. 2)
      • 5. Boucher, B.: ‘Lowering the cost of project using simple analysis of SCADA data – a real case example’. PHM Conf., New Orleans, 2013.
    3. 3)
      • 1. Tavner, P.J., Ran, L., Penman, J., Sedding, H.: ‘Condition monitoring of rotating electrical machines’ (Institution of Engineering and Technology, London, 2008), Series: IET Power and Energy.
    4. 4)
    5. 5)
    6. 6)
      • 9. Catmul, S.: ‘Self-organising map based condition monitoring of wind turbines’. Proc. 2011 European Wind Energy Association Annual Event (EWEA 2011), Brussels, 2011.
    7. 7)
      • 3. Harman, K., Walker, R., Wilkinson, M.: ‘Availability trends observed at operational wind farms’. Proc. 2008 European Wind Energy Conf. (EWEC 2008), Brussels, 2008.
    8. 8)
    9. 9)
    10. 10)
      • 11. Feng, Y., Tavner, P.J., Crabtree, C.J., Feng, Y., Qui, Y.: ‘Use of SCADA and CMS signals for failure detection and diagnosis of a wind turbine gearbox’. Proc. 2011 European Wind Energy Association Annual Event (EWEA 2011), Brussels, 2011.
    11. 11)
      • 8. Kohonen, T.: ‘Self-organising maps’ (Springer, 2001, 3rd edn.).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rpg.2013.0318
Loading

Related content

content/journals/10.1049/iet-rpg.2013.0318
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading