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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/22713

Title: Using SCADA data for wind turbine condition monitoring - a review
Authors: Tautz-Weinert, Jannis
Watson, Simon J.
Issue Date: 2017
Publisher: © Institution of Engineering and Technology
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
Abstract: 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.
Description: 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.
Sponsor: 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.
Version: Accepted for publication
DOI: 10.1049/iet-rpg.2016.0248
URI: https://dspace.lboro.ac.uk/2134/22713
Publisher Link: http://dx.doi.org/10.1049/iet-rpg.2016.0248
ISSN: 1752-1416
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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