000074858 001__ 74858
000074858 005__ 20200117211559.0
000074858 0247_ $$2doi$$a10.1088/1742-6596/1037/6/062003
000074858 0248_ $$2sideral$$a107158
000074858 037__ $$aART-2018-107158
000074858 041__ $$aeng
000074858 100__ $$0(orcid)0000-0002-3830-9308$$aReder, M.
000074858 245__ $$aA Bayesian Approach for Predicting Wind Turbine Failures based on Meteorological Conditions
000074858 260__ $$c2018
000074858 5060_ $$aAccess copy available to the general public$$fUnrestricted
000074858 5203_ $$aWith the growing wind energy sector, the need for advanced operation and maintenance (O&M) strategies has emerged. So far, mainly corrective or preventive O&M actions are applied. Predictive modelling, however, is expected to significantly enhance existing O&M practice. Here, anticipating wind turbine component failures can enable operators to lower the O&M cost and is particularly useful for wind farms located in remote areas or offshore locations. Previous research has shown that the failure behaviour of wind turbines and their components is highly influenced by the meteorological conditions under which the turbines operate. Hence, there is a significant need for robust models for failure prediction taking into consideration these conditions. Furthermore, solutions need to be found in order to determine the most suitable input variables for enhancing their prediction accuracy. This study uses failure data obtained from 984 wind turbines during 87 operational WT years. Bayesian belief networks (BBN) are trained based on failure records, technology specific covariates, as well as measurements of the environmental and operational conditions at site. Subsequently, the failure events in a wind farm during a period of 36 months are predicted with the BNN, whereas the failure events of six main components are predicted separately. Furthermore, an extensive sensitivity study is carried out to find the model with the highest prediction accuracy for each component. The influence of each meteorological, operational or technical covariate are discussed in detail. The models achieved a very good accuracy and were able to predict the majority of the component failures over the prediction period.
000074858 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000074858 592__ $$a0.221$$b2018
000074858 593__ $$aPhysics and Astronomy (miscellaneous)$$c2018$$dQ3
000074858 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000074858 700__ $$0(orcid)0000-0003-2360-0845$$aMelero, J.J.$$uUniversidad de Zaragoza
000074858 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000074858 773__ $$g1037 (2018), 062003 [11 pp]$$pJ. Phys.: Conf. Ser.$$tJournal of physics. Conference series$$x1742-6588
000074858 8564_ $$s3498138$$uhttps://zaguan.unizar.es/record/74858/files/texto_completo.pdf$$yVersión publicada
000074858 8564_ $$s13748$$uhttps://zaguan.unizar.es/record/74858/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000074858 909CO $$ooai:zaguan.unizar.es:74858$$particulos$$pdriver
000074858 951__ $$a2020-01-17-21:10:57
000074858 980__ $$aARTICLE