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Fault prediction for power transformer using optical spectrum of transformer oil and data mining analysis

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dc.citedby8
dc.contributor.authorFauzi N.A.en_US
dc.contributor.authorAli N.H.N.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorThiviyanathan V.A.en_US
dc.contributor.authorLeong Y.S.en_US
dc.contributor.authorSabry A.H.en_US
dc.contributor.authorJamaludin M.D.Z.B.en_US
dc.contributor.authorLo C.K.en_US
dc.contributor.authorMun L.H.en_US
dc.contributor.authorid57205073909en_US
dc.contributor.authorid57196922007en_US
dc.contributor.authorid37461740800en_US
dc.contributor.authorid57205077992en_US
dc.contributor.authorid57202929965en_US
dc.contributor.authorid56602511900en_US
dc.contributor.authorid57216839721en_US
dc.contributor.authorid36721595300en_US
dc.contributor.authorid6507460925en_US
dc.date.accessioned2023-05-29T08:12:38Z
dc.date.available2023-05-29T08:12:38Z
dc.date.issued2020
dc.descriptionCost effectiveness; Data mining; Electric discharges; Fault detection; Forecasting; Oil tanks; Optical correlation; Optical materials; Power transformers; Spectrum analysis; Voltage control; Data analytic tools; Dissolved gas analysis; Electrical discharges; On- load tap changers; Optical characteristics; Optical spectroscopy; Optical spectroscopy techniques; Periodic preventive maintenance; Oil filled transformersen_US
dc.description.abstractPeriodic preventive maintenance of power transformer should be conducted for its health monitoring and early fault detection. Transformer oil is a vital element where its contents and properties need to be monitored during the service life of a power transformer. This paper presents an optical spectroscopy measurement from 200 nm to 3300 nm to characterize the transformer oil, which were sampled from the main tanks and 'on-load tap changer' of power transformers. The correlation of the optical characteristics in the range of 2120 nm to 2220 nm to the Dissolved Gas Analysis results and Duval Triangle interpretation demonstrates that the low energy electrical discharges, high energy electrical discharges as well as the thermal faults rated at temperatures above 700�C in power transformers can be accurately predicted. For faster and accurate analysis of fault prediction, a data mining analytics tool was constructed using Rapid Miner server to analyze and verify the predictions for a total of 108 oil samples. For the optimization, continuous iterations were performed to determine the best absorbance-wavelength combination that can improve the accuracy of the prediction. The performance of the optical spectroscopy technique integrated with data analytic tool was analyzed and it was found that the technique contributes to a high accuracy of 98.1% in fault prediction. It is a cost-effective and quicker complementing approach to carry out pre-screening of the transformer oil in order to know the condition of the power transformers based on the transformer oil's optical characteristics. � 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9146611
dc.identifier.doi10.1109/ACCESS.2020.3011504
dc.identifier.epage136381
dc.identifier.scopus2-s2.0-85090389019
dc.identifier.spage136374
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090389019&doi=10.1109%2fACCESS.2020.3011504&partnerID=40&md5=355313318a182f4a9a2bc63fa20bd7b6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25679
dc.identifier.volume8
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.titleFault prediction for power transformer using optical spectrum of transformer oil and data mining analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
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