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Automated wind turbine maintenance scheduling

journal contribution
posted on 27.04.2020, 12:59 by NY Yürüşen, Paul Rowley, SJ Watson, JJ Melero
© 2020 Elsevier Ltd While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.

Funding

European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 642108, known as the AWESOME consortium

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Research Unit

  • Centre for Renewable Energy Systems Technology (CREST)

Published in

Reliability Engineering and System Safety

Volume

200

Pages

106965

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Reliability Engineering and System Safety and the definitive published version is available at https://doi.org/10.1016/j.ress.2020.106965

Acceptance date

31/03/2020

Publication date

2020-04-03

Copyright date

2020

ISSN

0951-8320

Language

en

Depositor

Dr Paul Rowley deposit date: 25 April 2020

Article number

106965

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