Please use this identifier to cite or link to this item: http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76848
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dc.contributor.authorChia, Kwang Tan-
dc.contributor.authorJun, Ying Wong-
dc.contributor.authorYuh, Ru Wong-
dc.contributor.authorYogendra A/L Balasubramaniam-
dc.contributor.authorChong, Tak Yaw-
dc.contributor.authorSiaw, Paw Koh-
dc.contributorCollege of Graduate Studies, Universiti Tenaga Nasional (UNITEN)en_US
dc.contributorInstitute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN)en_US
dc.contributorDepartment of Electrical and Electronics Engineering, Universiti Tenaga Nasional (UNITEN)en_US
dc.contributorDepartment Higher Institution Centre of Excellence (HiCoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC)en_US
dc.creatorYogendra A/L Balasubramaniam-
dc.creatorChong, Tak Yaw-
dc.creatorSiaw, Paw Koh-
dc.date.accessioned2022-11-10T01:03:18Z-
dc.date.available2022-11-10T01:03:18Z-
dc.date.issued2022-
dc.identifier.citationJournal of Engineering Research and Education, vol.14, 2022, pages 44-54en_US
dc.identifier.issn1823-2981 (print)-
dc.identifier.issn2232-1098 (online)-
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/76848-
dc.descriptionLink to publisher's homepage at http://jere.unimap.edu.myen_US
dc.description.abstractEnsuring good conditions and functionalities of these power transformers, these units are constantly monitored and maintained through the implementation of various conditionbased maintenance activities. However, despite all of these preventive maintenance practices in place, some transformer defects are still left undetected, especially at an early stage. There is a lack of a holistic risk evaluation system in the power utility company to support and guide the scheduling and prioritization of condition-based maintenance activities. It is reported that there was a total of 20 power transformer failure cases during the years 2005-2019. These failures led to higher operating expenses, arising from the cost of repair and loss of revenues due to outages and downtime. As such, the outcome of this research aims to fill in this gap in the preventive maintenance system currently in practice in the power utility company by developing a transformer failure prediction system to complement the existing maintenance testing activities that are performed routinely as a part of condition-based maintenance in Malaysia. A Tier 1 to Tier 2 prediction algorithm is developed in this project with the help of artificial intelligence to accelerate the availability of Tier 2 electrical test results. This allows early assessment of the transformer's electrical parameters. Thereafter, the predicted Tier 2 test results can be used in conjunction with transformer age, loading, visual inspection as well as Tier 1 oil test results to predict failure probability and fault type through the development of a lookup table. Overall, this algorithm aims to speed up and improve the transformer health assessment to act as an early warning system for future tripping and failure events. This allows condition-based maintenance activities that are currently in practice to prioritize transformers that are undergoing more severe deterioration before permanent irreversible damage occurs.en_US
dc.language.isoenen_US
dc.publisherUniversiti Malaysia Perlis (UniMAP)en_US
dc.subject.otherCondition-Based Maintenanceen_US
dc.subject.otherDistribution Networken_US
dc.subject.otherFailureen_US
dc.subject.otherMachine Learningen_US
dc.subject.otherPredictionen_US
dc.subject.otherTransformeren_US
dc.titlePower transformer health prediction using machine learningen_US
dc.typeArticleen_US
dc.contributor.urlchongty@uniten.edu.myen_US
dc.contributor.urljohnnykoh@uniten.edu.myen_US
dc.contributor.urlyogendra@tnb.com.myen_US
Appears in Collections:Journal of Engineering Research and Education (JERE)

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