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dc.contributor.authorCen, Zhaohui
dc.contributor.authorStewart, Paul
dc.date.accessioned2016-11-25T11:41:12Z
dc.date.available2016-11-25T11:41:12Z
dc.date.issued2016-10-31
dc.identifier.citationCen, Z. and Stewart, P. (2016) 'Condition Parameter Estimation for Photovoltaic Buck Converters Based on Adaptive Model Observers', IEEE Transactions on Reliability, Vol. PP, Issue 99, DOI: 10.1109/TR.2016.2618320en
dc.identifier.issn0018-9529
dc.identifier.doi10.1109/TR.2016.2618320
dc.identifier.urihttp://hdl.handle.net/10545/621063en
dc.description.abstractDC-DC power converters such as buck converters are susceptible to degradation and failure due to operating under conditions of electrical stress and variable power sources in power conversion applications, such as electric vehicles and renewable energy. Some key components such as electrolytic capacitors degrade over time due to evaporation of the electrolyte. In this paper, a model-observer based scheme is proposed to monitor the states of Buck converters and to estimate their component parameters, such as capacitance and inductance. First, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Second, component condition parameters, such as capacitance and inductance are reconstructed using another novel observer with adaptive feedback law. Additionally, the observer structures and their theoretical performance are analyzed and proven. In contrast to existing reliability approaches applied in buck converters, the proposed scheme performs online-estimation for key parameters. Finally, buck converters in conventional dc–dc step-down and photovoltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiments. Results demonstrate the feasibility, performance, and superiority of the proposed component parameter estimation scheme.
dc.description.sponsorshipThis paper is an associated output from EU ERDF funded programs Enscite, Enabling Innovation and Low Carbonen
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7726061/en
dc.rightsArchived with thanks to IEEE Transactions on Reliabilityen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectObserversen
dc.subjectPower electronicsen
dc.subjectCapacitorsen
dc.subjectInductorsen
dc.titleCondition parameter estimation for photovoltaic buck converters based on adaptive model observersen
dc.typeArticleen
dc.identifier.eissn1558-1721
dc.contributor.departmentDerby Universityen
dc.identifier.journalIEEE Transactions on Reliabilityen
refterms.dateFOA2017-11-01T00:00:00Z
html.description.abstractDC-DC power converters such as buck converters are susceptible to degradation and failure due to operating under conditions of electrical stress and variable power sources in power conversion applications, such as electric vehicles and renewable energy. Some key components such as electrolytic capacitors degrade over time due to evaporation of the electrolyte. In this paper, a model-observer based scheme is proposed to monitor the states of Buck converters and to estimate their component parameters, such as capacitance and inductance. First, a diagnosis observer is proposed, and the generated residual vectors are applied for fault detection and isolation. Second, component condition parameters, such as capacitance and inductance are reconstructed using another novel observer with adaptive feedback law. Additionally, the observer structures and their theoretical performance are analyzed and proven. In contrast to existing reliability approaches applied in buck converters, the proposed scheme performs online-estimation for key parameters. Finally, buck converters in conventional dc–dc step-down and photovoltaic applications are investigated to test and validate the effectiveness of the proposed scheme in both simulation and laboratory experiments. Results demonstrate the feasibility, performance, and superiority of the proposed component parameter estimation scheme.


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