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dc.contributor.authorOdofin, Sarah
dc.contributor.authorBentley, Edward
dc.contributor.authorAikhuele, Daniel
dc.date.accessioned2018-01-30T16:15:23Z
dc.date.available2018-01-30T16:15:23Z
dc.date.issued2017-12-15
dc.identifier.citationOdofin, S. et al (2018) 'Robust fault estimation for wind turbine energy via hybrid systems', Renewable Energy, Vol. 120, pp. 289.- 299.en
dc.identifier.issn09601481
dc.identifier.doi10.1016/j.renene.2017.12.031
dc.identifier.urihttp://hdl.handle.net/10545/622102
dc.description.abstractThe rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0960148117312351en
dc.rightsArchived with thanks to Renewable Energyen
dc.subjectFault estimationen
dc.subjectWind turbinesen
dc.subjectEigenstructureen
dc.subjectGenetic algorithmsen
dc.subjectOptimisationen
dc.subjectAugmented perceptionen
dc.titleRobust fault estimation for wind turbine energy via hybrid systems.en
dc.typeArticleen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentNothumbria Universityen
dc.contributor.departmentBells University of Technologyen
dc.identifier.journalRenewable Energyen
refterms.dateFOA2018-12-15T00:00:00Z
html.description.abstractThe rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.


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