Publication:
Application of markov model to estimate individual condition parameters for transformers

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dc.citedby10
dc.contributor.authorSelva A.M.en_US
dc.contributor.authorAzis N.en_US
dc.contributor.authorYahaya M.S.en_US
dc.contributor.authorAb Kadir M.Z.A.en_US
dc.contributor.authorJasni J.en_US
dc.contributor.authorGhazali Y.Z.Y.en_US
dc.contributor.authorTalib M.A.en_US
dc.contributor.authorid57203742582en_US
dc.contributor.authorid56120698200en_US
dc.contributor.authorid36083783000en_US
dc.contributor.authorid25947297000en_US
dc.contributor.authorid25632671500en_US
dc.contributor.authorid55336569600en_US
dc.contributor.authorid36609320500en_US
dc.date.accessioned2023-05-29T06:56:26Z
dc.date.available2023-05-29T06:56:26Z
dc.date.issued2018
dc.descriptionCarbon dioxide; Carbon monoxide; Electric breakdown; Ethylene; IEEE Standards; Markov processes; Nonlinear programming; Probability distributions; Statistical tests; Absolute error; Chi-square tests; Condition parameters; Condition-based monitoring; Markov model; Non-linear optimization; Parameter estimationen_US
dc.description.abstractThis paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and computed condition parameters. It is found that estimation for majority of the individual condition parameter of the transformer population can be carried out by MM. The Chi-square test reveals that apart from CH4 and C2H4, the condition parameters are outside the rejection region that indicates agreement between predicted and computed values. It is also observed that the lowest and highest percentages of differences between predicted and computed values of all the condition parameters are 81.46% and 98.52%, respectively. � 2018 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo2114
dc.identifier.doi10.3390/en11082114
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85052822566
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85052822566&doi=10.3390%2fen11082114&partnerID=40&md5=5ed4887c5ad9d19d521c2c157e8ff4c6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24176
dc.identifier.volume11
dc.publisherMDPI AGen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleApplication of markov model to estimate individual condition parameters for transformersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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