000101164 001__ 101164 000101164 005__ 20210902121709.0 000101164 0247_ $$2doi$$a10.1016/j.ress.2020.106965 000101164 0248_ $$2sideral$$a117682 000101164 037__ $$aART-2020-117682 000101164 041__ $$aeng 000101164 100__ $$0(orcid)0000-0002-1206-9756$$aYürüsen, Nurseda Y. 000101164 245__ $$aAutomated wind turbine maintenance scheduling 000101164 260__ $$c2020 000101164 5060_ $$aAccess copy available to the general public$$fUnrestricted 000101164 5203_ $$aWhile many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day. 000101164 536__ $$9info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 642108-AWESOME 000101164 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000101164 590__ $$a6.188$$b2020 000101164 591__ $$aOPERATIONS RESEARCH & MANAGEMENT SCIENCE$$b11 / 84 = 0.131$$c2020$$dQ1$$eT1 000101164 591__ $$aENGINEERING, INDUSTRIAL$$b11 / 49 = 0.224$$c2020$$dQ1$$eT1 000101164 592__ $$a1.76$$b2020 000101164 593__ $$aApplied Mathematics$$c2020$$dQ1 000101164 593__ $$aSafety, Risk, Reliability and Quality$$c2020$$dQ1 000101164 593__ $$aIndustrial and Manufacturing Engineering$$c2020$$dQ1 000101164 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000101164 700__ $$aRowley, Paul N. 000101164 700__ $$aWatson, Simon J. 000101164 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, Julio J.$$uUniversidad de Zaragoza 000101164 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica 000101164 773__ $$g200 (2020), 106965 [14 pp.]$$pReliab. eng. syst. saf.$$tReliability Engineering and System Safety$$x0951-8320 000101164 8564_ $$s1635809$$uhttps://zaguan.unizar.es/record/101164/files/texto_completo.pdf$$yPostprint 000101164 8564_ $$s1522870$$uhttps://zaguan.unizar.es/record/101164/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000101164 909CO $$ooai:zaguan.unizar.es:101164$$particulos$$pdriver 000101164 951__ $$a2021-09-02-09:19:18 000101164 980__ $$aARTICLE