000079728 001__ 79728
000079728 005__ 20200716101516.0
000079728 0247_ $$2doi$$a10.1016/j.renene.2018.07.068
000079728 0248_ $$2sideral$$a107498
000079728 037__ $$aART-2019-107498
000079728 041__ $$aeng
000079728 100__ $$0(orcid)0000-0002-3698-6284$$aGonzalez, E.
000079728 245__ $$aUsing high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study
000079728 260__ $$c2019
000079728 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079728 5203_ $$aIntensive condition monitoring of wind generation plant through analysis of routinely collected SCADA data is seen as a viable means of forestalling costly plant failure and optimising maintenance through identification of failure at the earliest possible stage. The challenge to operators is in identifying the signatures of failure within data streams and disambiguating these from other operational factors. The well understood power curve representation of turbine performance offers an intuitive and quantitative means of identifying abnormal operation, but only if noise and artefacts of operating regime change can be excluded. In this paper, a methodology for wind turbine performance monitoring based on the use of high-frequency SCADA data is employed featuring state-of-the-art multivariate non-parametric methods for power curve modelling. The model selection considerations for these are examined together with their sensitivity to several factors, including site specific conditions, seasonality effects, input relevance and data sampling rate. The results, based on operational data from four wind farms, are discussed in a practical context with the use of high frequency data demonstrated to be beneficial for performance monitoring purposes whereas further attention is required in the area of expressing model uncertainty.
000079728 536__ $$9info:eu-repo/grantAgreement/EC/H2020/642108/EU/Advanced Wind Energy Systems Operation and Maintenance Expertise/AWESOME$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 642108-AWESOME
000079728 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000079728 590__ $$a6.274$$b2019
000079728 591__ $$aGREEN & SUSTAINABLE SCIENCE & TECHNOLOGY$$b9 / 41 = 0.22$$c2019$$dQ1$$eT1
000079728 591__ $$aENERGY & FUELS$$b19 / 112 = 0.17$$c2019$$dQ1$$eT1
000079728 592__ $$a2.052$$b2019
000079728 593__ $$aRenewable Energy, Sustainability and the Environment$$c2019$$dQ1
000079728 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000079728 700__ $$aStephen, B.
000079728 700__ $$aInfield, D.
000079728 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, J.J.$$uUniversidad de Zaragoza
000079728 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000079728 773__ $$g131 (2019), 841-853$$pRenew. energy$$tRenewable Energy$$x0960-1481
000079728 8564_ $$s1266015$$uhttps://zaguan.unizar.es/record/79728/files/texto_completo.pdf$$yPostprint
000079728 8564_ $$s57424$$uhttps://zaguan.unizar.es/record/79728/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000079728 909CO $$ooai:zaguan.unizar.es:79728$$particulos$$pdriver
000079728 951__ $$a2020-07-16-09:23:09
000079728 980__ $$aARTICLE