69643 20200108100348.0 doi 10.1016/j.ress.2017.10.004 sideral 101763 ART-2018-101763 eng (orcid)0000-0002-3830-9308 Reder, Maik Data-driven learning framework for associating weather conditions and wind turbine failures 2018 Access copy available to the general public Unrestricted The need for cost effective operation and maintenance (O&M) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed. 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/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 4.039 2018 OPERATIONS RESEARCH & MANAGEMENT SCIENCE 11 / 84 = 0.131 2018 Q1 T1 ENGINEERING, INDUSTRIAL 5 / 46 = 0.109 2018 Q1 T1 1.944 2018 Applied Mathematics 2018 Q1 Safety, Risk, Reliability and Quality 2018 Q1 Industrial and Manufacturing Engineering 2018 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Yürüsen, Nurseda Y. (orcid)0000-0003-2360-0845 Melero, Julio J. Universidad de Zaragoza 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 169 (2018), 554-559 Reliab. eng. syst. saf. RELIABILITY ENGINEERING & SYSTEM SAFETY 0951-8320 3393976 http://zaguan.unizar.es/record/69643/files/texto_completo.pdf Preprint 74625 http://zaguan.unizar.es/record/69643/files/texto_completo.jpg?subformat=icon icon Preprint oai:zaguan.unizar.es:69643 articulos driver 2020-01-08-09:31:28 ARTICLE