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Título:
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Nonlinear set-membership identification and fault detection using a Bayesian framework: appllication to the wind turbine benchmark
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Autor/a:
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Fernández Canti, Rosa M.; Tornil Sin, Sebastián; Blesa Izquierdo, Joaquim; Puig Cayuela, Vicenç
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Otros autores:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control; Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
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Abstract:
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This paper deals with the problem of nonlinear set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation can be reformulated from a Bayesian viewpoint in order to determine the feasible parameter set and, in a posterior fault detection stage, to check the consistency between the model and the data. The paper shows that the Bayesian approach, assuming uniform distributed measurement noise and flat model prior probability distribution, leads to the same feasible parameter set as the set-membership technique. To illustrate this point a comparison with the subpavings approach is included. Finally, by means of the application to the wind turbine benchmark problem, it is shown how the Bayesian fault detection test works successfully. |
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Abstract:
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Peer Reviewed |
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Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control
-Àrees temàtiques de la UPC::Energies::Energia eòlica::Aerogeneradors
-Wind turbines -- Automatic control
-Nonlinear set-membership identification
-Fault detection
-Wind turbine
-Energia eòlica |
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Derechos:
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Tipo de documento:
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Artículo - Versión publicada
Objeto de conferencia |
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Editor:
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Institute of Electrical and Electronics Engineers (IEEE)
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Compartir:
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