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Automated wind turbine maintenance scheduling
journal contributionposted on 27.04.2020 by NY Yürüşen, Paul Rowley, SJ Watson, JJ Melero
Any type of content formally published in an academic journal, usually following a peer-review process.
© 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.
European Union’s Horizon 2020 research and innovation programme under the Marie Sk lodowska-Curie grant agreement No 642108, known as the AWESOME consortium
- Mechanical, Electrical and Manufacturing Engineering
- Centre for Renewable Energy Systems Technology (CREST)