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SCADA and Artificial neural networks for maintenance management

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dc.contributor.author Pliego Marugán, Alberto
dc.contributor.author García Márquez, Fausto Pedro
dc.date.accessioned 2017-12-15T15:20:11Z
dc.date.available 2017-12-15T15:20:11Z
dc.date.issued 2017-06-29
dc.identifier.citation Lecture Notes on Multidisciplinary Industrial Engineering es_ES
dc.identifier.isbn 978-3-319-59279-4
dc.identifier.uri http://hdl.handle.net/10578/16081
dc.description.abstract Nowadays, the reliability of the wind turbines is essential to ensure the efficiency and the benefits of the wind energy. The SCADA system installed in a wind turbine generates lot of data that need to be processed. The information obtained from these data can be used for improving the operation and management, obtaining more reliable systems. The SCADA systems operate through different control rules that are predefined. However, a static control of the wind turbine can generate a miscorrelation between the control and the real conditions of the wind turbine. For example, two wind turbines can be separated several kilometers in the same wind farm, therefore, the operation conditions must be different and the control strategy should not be unique. This research work presents a method based on neural networks for a dynamic generation of the control strategy. The method suggests that the thresholds used for generating alarms can vary and, therefore, the control of the wind turbine will be adapted to each specific wind turbine. es_ES
dc.format application/pdf es_ES
dc.language.iso en es_ES
dc.rights info:eu-repo/semantics/openAccess es_ES
dc.subject Maintenance managemen es_ES
dc.subject Wind Turbine es_ES
dc.subject Reliability es_ES
dc.subject SCADA systems es_ES
dc.subject Artificial neural networks es_ES
dc.subject Advanced control analytics es_ES
dc.title SCADA and Artificial neural networks for maintenance management es_ES
dc.type info:eu-repo/semantics/bookPart es_ES
dc.relation.projectID DPI2015-67264-P. es_ES
dc.identifier.DOI https://doi.org/10.1007/978-3-319-59280-0_75


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