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Power transformer health prediction using machine learning

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Main Authors: Yogendra A/L Balasubramaniam, Chong, Tak Yaw, Siaw, Paw Koh, Chia, Kwang Tan, Jun, Ying Wong, Yuh, Ru Wong
Other Authors: chongty@uniten.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Condition-Based Maintenance
Distribution Network
Failure
Machine Learning
Prediction
Transformer
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76848
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spelling my.unimap-768482022-11-10T01:03:18Z Power transformer health prediction using machine learning Yogendra A/L Balasubramaniam Chong, Tak Yaw Siaw, Paw Koh Chia, Kwang Tan Jun, Ying Wong Yuh, Ru Wong Yogendra A/L Balasubramaniam Chong, Tak Yaw Siaw, Paw Koh chongty@uniten.edu.my johnnykoh@uniten.edu.my yogendra@tnb.com.my College of Graduate Studies, Universiti Tenaga Nasional (UNITEN) Institute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN) Department of Electrical and Electronics Engineering, Universiti Tenaga Nasional (UNITEN) Department Higher Institution Centre of Excellence (HiCoE), UM Power Energy Dedicated Advanced Centre (UMPEDAC) Condition-Based Maintenance Distribution Network Failure Machine Learning Prediction Transformer Link to publisher's homepage at http://jere.unimap.edu.my Ensuring 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. 2022-11-10T01:03:18Z 2022-11-10T01:03:18Z 2022 Article Journal of Engineering Research and Education, vol.14, 2022, pages 44-54 1823-2981 (print) 2232-1098 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76848 en Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Condition-Based Maintenance
Distribution Network
Failure
Machine Learning
Prediction
Transformer
spellingShingle Condition-Based Maintenance
Distribution Network
Failure
Machine Learning
Prediction
Transformer
Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
Chia, Kwang Tan
Jun, Ying Wong
Yuh, Ru Wong
Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
Power transformer health prediction using machine learning
description Link to publisher's homepage at http://jere.unimap.edu.my
author2 chongty@uniten.edu.my
author_facet chongty@uniten.edu.my
Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
Chia, Kwang Tan
Jun, Ying Wong
Yuh, Ru Wong
Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
format Article
author Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
Chia, Kwang Tan
Jun, Ying Wong
Yuh, Ru Wong
Yogendra A/L Balasubramaniam
Chong, Tak Yaw
Siaw, Paw Koh
author_sort Yogendra A/L Balasubramaniam
title Power transformer health prediction using machine learning
title_short Power transformer health prediction using machine learning
title_full Power transformer health prediction using machine learning
title_fullStr Power transformer health prediction using machine learning
title_full_unstemmed Power transformer health prediction using machine learning
title_sort power transformer health prediction using machine learning
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/76848
_version_ 1751537977173999616
score 13.222552

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