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dc.contributor.authorLiang, Z
dc.contributor.authorParlikad, A
dc.date.accessioned2018-03-12T12:04:49Z
dc.date.available2018-03-12T12:04:49Z
dc.date.issued2018
dc.identifier.issn0142-0615
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/273912
dc.description.abstractThe condition of the insulation paper is one of the key determinants of the lifetime of a power transformer. The winding insulation paper may deteriorate aggressively and result in the unexpected failure of power transformers, especially under the presence of high moisture, oxygen, and metal contaminants. Such types of scenarios can be prevented if the deterioration is detected on time. Various types of condition monitoring techniques have been developed to detect transformer condition such as dissolved gas analysis and frequency response analysis. They are non-intrusive and provide early warning of accelerated deterioration both chemically and mechanically. However, the accuracy of those techniques is imperfect, which means periodic inspection is still indispensable. In this paper, we discuss the value of continuous condition monitoring for power transformers and present a way to estimate this value. Towards this, a continuous-time Markov decision model is presented to optimize periodic inspections, so that the cost is minimized and the availability is maximized. We also analyze the performance based on the information from both discrete inspection and continuous condition monitoring. The result shows the dissolved gas analysis can improve the availability and operation cost, while frequency response analysis can only improve the availability of power transformers.
dc.description.sponsorshipEPSRC, Innovate UK
dc.publisherElsevier BV
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA Markovian model for power transformer maintenance
dc.typeArticle
prism.endingPage182
prism.publicationDate2018
prism.publicationNameInternational Journal of Electrical Power and Energy Systems
prism.startingPage175
prism.volume99
dc.identifier.doi10.17863/CAM.20989
dcterms.dateAccepted2017-12-24
rioxxterms.versionofrecord10.1016/j.ijepes.2017.12.024
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-07-01
dc.contributor.orcidLiang, Zhenglin [0000-0003-2572-9423]
dc.contributor.orcidParlikad, Ajith [0000-0001-6214-1739]
dc.identifier.eissn1879-3517
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) Marie Sk?odowska-Curie actions (645733)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/N021614/1)
pubs.funder-project-idTechnology Strategy Board (920035)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/L010917/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K000314/1)
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/I019308/1)
cam.issuedOnline2018-02-20
cam.orpheus.successThu Jan 30 12:59:49 GMT 2020 - The item has an open VoR version.
rioxxterms.freetoread.startdate2100-01-01


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International