A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7034
comunitat-uji-handle3:10234/8619
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INVESTIGACIONMetadatos
Título
A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farmsFecha de publicación
2019-08-30Editor
The Institution of Engineering and Technology (IET)Cita bibliográfica
SALES-SETIEN, Ester; PEÑARROCHA, Ignacio. A Markovian jump system approach for the estimation and adaptive diagnosis of decreased power generation in wind farms. IET Control Theory & Applications, 2019.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://digital-library.theiet.org/content/journals/10.1049/iet-cta.2018.6199Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer ... [+]
In this study, a Markovian jump model of the power generation system of a wind turbine is proposed and the authors present a closed-loop model-based observer to estimate the faults related to energy losses. The observer is designed through an H∞-based optimisation problem that optimally fixes the trade-off between the observer fault sensitivity and robustness. The fault estimates are then used in data-based decision mechanisms for achieving fault detection and isolation. The performance of the strategy is then ameliorated in a wind farm (WF) level scheme that uses a bank of the aforementioned observers and decision mechanisms. Finally, the proposed approach is tested using a well-known benchmark in the context of WF fault diagnosis. [-]
Proyecto de investigación
Spanish MECD (Grant FPU14/01592) ; Spanish MINECO (Project TEC2015-69155-R) ; Universitat Jaume I (Project P11B2015-42)Derechos de acceso
© 2019 The Institution of Engineering and Technology
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