101164 20210902121709.0 doi 10.1016/j.ress.2020.106965 sideral 117682 ART-2020-117682 eng Yürüsen, Nurseda Y. (orcid)0000-0002-1206-9756 Automated wind turbine maintenance scheduling 2020 Access copy available to the general public Unrestricted 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. info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 642108-AWESOME info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 6.188 2020 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 11 / 84 = 0.131 2020 Q1 T1 ENGINEERING, INDUSTRIAL 11 / 49 = 0.224 2020 Q1 T1 1.76 2020 Applied Mathematics 2020 Q1 Safety, Risk, Reliability and Quality 2020 Q1 Industrial and Manufacturing Engineering 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Rowley, Paul N. Watson, Simon J. Melero, Julio J. Universidad de Zaragoza (orcid)0000-0003-2360-0845 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 200 (2020), 106965 [14 pp.] Reliab. eng. syst. saf. Reliability Engineering and System Safety 0951-8320 1635809 http://zaguan.unizar.es/record/101164/files/texto_completo.pdf Postprint 1522870 http://zaguan.unizar.es/record/101164/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:101164 articulos driver 2021-09-02-09:19:18 ARTICLE