Gonzalez_2018_J._Phys.%3A_Conf._Ser._1037_032038.pdf (2.67 MB)

Statistical evaluation of SCADA data for wind turbine condition monitoring and farm assessment

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journal contribution
posted on 03.07.2018 by E. Gonzalez, Jannis Weinert, J.J. Melero, Simon J. Watson
Operational data from wind farms is crucial for wind turbine condition monitoring and performance assessment. In this paper, we analyse three wind farms with the aim to monitor environmental and operational conditions that might result in underperformance or failures. The assessment includes a simple wind speed characterisation and wake analysis. The evolution of statistical parameters is used to identify anomalous turbine behaviour. In total, 88 turbines and 12 failures are analysed, covering different component failures. Notwithstanding the short period of data available, several operational parameters are found to deviate from the farm trend in some turbines affected by failures. As a result, some parameters show better monitoring capabilities than others, for the detection of certain failures. However, the limitations of SCADA statistics are also shown as not all failures showed anomalies in the observed parameters.

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 642108.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Journal of Physics: Conference Series

Volume

1037

Citation

GONZALEZ, E. ...et al., 2018. Statistical evaluation of SCADA data for wind turbine condition monitoring and farm assessment. Journal of Physics: Conference Series, 1037: 032038.

Publisher

IOP Publishing

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

Acceptance date

22/05/2018

Publication date

2018

Notes

This paper was presented at the Science of Making Torque from Wind (TORQUE) conference 2018, Milan, 20-22nd June. This is an Open Access Article. It is published by IOP under the Creative Commons Attribution 3.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/

ISSN

1742-6588

eISSN

1742-6596

Language

en

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