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
journal contributionposted on 2018-07-03, 09:00 authored 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.
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.
- Mechanical, Electrical and Manufacturing Engineering
Published inJournal of Physics: Conference Series
CitationGONZALEZ, E. ...et al., 2018. Statistical evaluation of SCADA data for wind turbine condition monitoring and farm assessment. Journal of Physics: Conference Series, 1037: 032038.
- VoR (Version of Record)
Publisher statementThis 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/
NotesThis 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/