79728 20200716101516.0 doi 10.1016/j.renene.2018.07.068 sideral 107498 ART-2019-107498 eng (orcid)0000-0002-3698-6284 Gonzalez, E. Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study 2019 Access copy available to the general public Unrestricted Intensive condition monitoring of wind generation plant through analysis of routinely collected SCADA data is seen as a viable means of forestalling costly plant failure and optimising maintenance through identification of failure at the earliest possible stage. The challenge to operators is in identifying the signatures of failure within data streams and disambiguating these from other operational factors. The well understood power curve representation of turbine performance offers an intuitive and quantitative means of identifying abnormal operation, but only if noise and artefacts of operating regime change can be excluded. In this paper, a methodology for wind turbine performance monitoring based on the use of high-frequency SCADA data is employed featuring state-of-the-art multivariate non-parametric methods for power curve modelling. The model selection considerations for these are examined together with their sensitivity to several factors, including site specific conditions, seasonality effects, input relevance and data sampling rate. The results, based on operational data from four wind farms, are discussed in a practical context with the use of high frequency data demonstrated to be beneficial for performance monitoring purposes whereas further attention is required in the area of expressing model uncertainty. 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/ 6.274 2019 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY 9 / 41 = 0.22 2019 Q1 T1 ENERGY & FUELS 19 / 112 = 0.17 2019 Q1 T1 2.052 2019 Renewable Energy, Sustainability and the Environment 2019 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Stephen, B. Infield, D. (orcid)0000-0003-2360-0845 Melero, J.J. Universidad de Zaragoza 5009 535 Universidad de Zaragoza Dpto. Ingeniería Eléctrica Área Ingeniería Eléctrica 131 (2019), 841-853 Renew. energy Renewable Energy 0960-1481 1266015 http://zaguan.unizar.es/record/79728/files/texto_completo.pdf Postprint 57424 http://zaguan.unizar.es/record/79728/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:79728 articulos driver 2020-07-16-09:23:09 ARTICLE