Publication:
Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model

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dc.citedby3
dc.contributor.authorSelva A.M.en_US
dc.contributor.authorYahaya M.S.en_US
dc.contributor.authorAzis N.en_US
dc.contributor.authorAb Kadir M.Z.A.en_US
dc.contributor.authorJasni J.en_US
dc.contributor.authorYang Ghazali Y.Z.en_US
dc.contributor.authorid57203742582en_US
dc.contributor.authorid36083783000en_US
dc.contributor.authorid56120698200en_US
dc.contributor.authorid25947297000en_US
dc.contributor.authorid25632671500en_US
dc.contributor.authorid55336569600en_US
dc.date.accessioned2023-05-29T06:51:45Z
dc.date.available2023-05-29T06:51:45Z
dc.date.issued2018
dc.descriptionElectric transformers; Health; Hidden Markov models; Nonlinear programming; Probability distributions; Quality control; Viterbi algorithm; Condition parameters; Dissolved gas analysis; Distribution transformer; Emission probabilities; Health indices; Non-linear optimization; Remaining useful lives; Transition probabilities; Parameter estimationen_US
dc.description.abstractThis paper presents a study to estimate future Health Index (HI) of transformer population based on Hidden Markov Model (HMM). In this paper, HI was represented as hidden state and the condition parameter factors in the HI algorithm namely Dissolved Gas Analysis Factor (DGAF), Oil Quality Analysis Factor (OQAF) and Furfural Analysis Factor (FAF) were represented as the observable states. A case study of 1130 oil samples from 373 oil-typed distribution transformers (33/11 kV and 30 MVA) were examined. First, the mean for HI in each year was computed and the transition probabilities for the condition data were obtained based on non-linear optimization technique. Next, the emission probabilities for each of the condition parameter factors were derived based on frequency of occurrence method. Subsequently, the future states probability distribution was computed based on the HMM prediction model and viterbi algorithm was applied to find the best optimal path sequence of HI for the respective observable condition. Finally, the predicted and computed HI were compared to the hypothesized distribution. Majority of the predicted HI agrees with computed HI. Predicted HI based on OQAF records the most accurate estimation throughout the sampling years. Inconsistencies are observed in year 2 and between year 7 and 10 for the predicted HI based on FAF. The predicted HI based on DGAF is in line with the computed HI during the first 2 years and deviates at the later stage of the sampling period. � 2018 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8684158
dc.identifier.doi10.1109/PECON.2018.8684158
dc.identifier.epage292
dc.identifier.scopus2-s2.0-85064749197
dc.identifier.spage288
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85064749197&doi=10.1109%2fPECON.2018.8684158&partnerID=40&md5=a26d32507d3602449f8d918060d51bc0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23775
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Green
dc.sourceScopus
dc.sourcetitle2018 IEEE 7th International Conference on Power and Energy, PECon 2018
dc.titleEstimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Modelen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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