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Using SCADA data for wind turbine condition monitoring - a review

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journal contribution
posted on 06.10.2016, 15:06 by Jannis Weinert, Simon Watson
The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine Supervisory Control And Data Acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring, focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine condition monitoring is discussed.

Funding

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

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

IET Renewable Power Generation

Citation

TAUTZ-WEINERT, J. and WATSON, S.J., 2017. Using SCADA data for wind turbine condition monitoring - a review. IET Renewable Power Generation, 11 (4), pp.382-394

Publisher

© Institution of Engineering and Technology

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

17/09/2016

Publication date

2017

Notes

This paper is a postprint of a paper submitted to and accepted for publication in IET Renewable Power Generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.

ISSN

1752-1416

eISSN

1752-1424

Language

en

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Keywords

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