Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty

Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the conditio...

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Autores principales:	Dhanu Rediansyah, Rahman Azis Prasojo, Suwarno, A. Abu-Siada
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Power transformer health index insulation system condition monitoring artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/58ab333cd1544f86b80e9a338198a4f6
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id	oai:doaj.org-article:58ab333cd1544f86b80e9a338198a4f6
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spelling	oai:doaj.org-article:58ab333cd1544f86b80e9a338198a4f62021-11-18T00:08:35ZArtificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty2169-353610.1109/ACCESS.2021.3125379https://doaj.org/article/58ab333cd1544f86b80e9a338198a4f62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9600888/https://doaj.org/toc/2169-3536Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.Dhanu RediansyahRahman Azis Prasojo SuwarnoA. Abu-SiadaIEEEarticlePower transformerhealth indexinsulation systemcondition monitoringartificial intelligenceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150637-150648 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Power transformer health index insulation system condition monitoring artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Power transformer health index insulation system condition monitoring artificial intelligence Electrical engineering. Electronics. Nuclear engineering TK1-9971 Dhanu Rediansyah Rahman Azis Prasojo Suwarno A. Abu-Siada Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
description	Power transformer is a critical and expensive asset in electric transmission and distribution networks. It is essential to monitor the health condition of all power transformer fleet in such networks to avoid unwanted outages. The health index (HI) is a quick and efficient way to assess the condition of power transformers based on multi-criteria. While Power transformer HI method has been well presented in the literature, not much attention was given to handle the uncertainty and reliability of this method due to unavailability of used data. Therefore, this paper aims to tackle this issue through employing Artificial Intelligence (AI)-based techniques to reveal the health condition of power transformers with high accuracy and at the same time handling data uncertainty. The proposed HI approach assesses the power transformer insulation system based on oil quality, dissolved gas analysis (DGA), and paper condition. In this regard, collected data from 504, 150-kV transformers are used to establish the proposed AI-models. Seven AI algorithms including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Decision Tree are investigated. A performance comparison of the proposed AI-based HI models is carried out using the scoring-weighting-based HI method as the reference. Results show that RF model provides the best performance in predicting power transformer HI with an accuracy of 97.3%.
format	article
author	Dhanu Rediansyah Rahman Azis Prasojo Suwarno A. Abu-Siada
author_facet	Dhanu Rediansyah Rahman Azis Prasojo Suwarno A. Abu-Siada
author_sort	Dhanu Rediansyah
title	Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
title_short	Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
title_full	Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
title_fullStr	Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
title_full_unstemmed	Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty
title_sort	artificial intelligence-based power transformer health index for handling data uncertainty
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/58ab333cd1544f86b80e9a338198a4f6
work_keys_str_mv	AT dhanurediansyah artificialintelligencebasedpowertransformerhealthindexforhandlingdatauncertainty AT rahmanazisprasojo artificialintelligencebasedpowertransformerhealthindexforhandlingdatauncertainty AT suwarno artificialintelligencebasedpowertransformerhealthindexforhandlingdatauncertainty AT aabusiada artificialintelligencebasedpowertransformerhealthindexforhandlingdatauncertainty
_version_	1718425226508763136

Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty

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