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Investigating performance of transformer health index in machine learning application using dominant features

Transformer Health Index (HI) has become a standard tool for performing transformer health evaluations. Due to economic constraints, the recently published paper focuses on developing various techniques to identify the most dominant features for transformer HI prediction. However, the fundamental pr...

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Main Authors: Mohmad, Azlan, Shapiai, M. Ibrahim, Shamsudin, M. Solehin, Abu, Mohd. Azlan, Abd. Hamid, Amirah
Format: Conference or Workshop Item
Language:English
Published: 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/96479/1/MohdAzlanAbu2021_InvestigatingPerformanceOfTransformerHealthIndex.pdf
http://eprints.utm.my/id/eprint/96479/
http://dx.doi.org/10.1088/1742-6596/2128/1/012025
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spelling my.utm.964792022-07-24T11:04:41Z http://eprints.utm.my/id/eprint/96479/ Investigating performance of transformer health index in machine learning application using dominant features Mohmad, Azlan Shapiai, M. Ibrahim Shamsudin, M. Solehin Abu, Mohd. Azlan Abd. Hamid, Amirah T Technology (General) Transformer Health Index (HI) has become a standard tool for performing transformer health evaluations. Due to economic constraints, the recently published paper focuses on developing various techniques to identify the most dominant features for transformer HI prediction. However, the fundamental problems concerning their input features remain unresolved since most suggested features contradict industry practice. In this paper, the primary objective is to investigate the performance of the transformer HI by developing and utilizing only dominant features following the industry recommendation. The investigated dominant features in this paper using 1) CO2/CO ratio and 2) the Incipient fault for detecting temperature abnormalities, and 3) the Dissipation Factor (DF) for detecting oil contamination. The performance validation is carried out using various machine learning (ML) classifiers. Also, the performance of the ML model is validated based on 10-fold type cross-validation to avoid biases in the experiment. As a result, the proposed Artificial Neural Network (ANN) network utilizing the investigated dominant features following the industry practice has produced the highest average accuracy of 80.09% than others ML techniques as a classifier. Hence, additional studies to complement the investigated dominant features may be considered for the subsequent investigation. 2021 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/96479/1/MohdAzlanAbu2021_InvestigatingPerformanceOfTransformerHealthIndex.pdf Mohmad, Azlan and Shapiai, M. Ibrahim and Shamsudin, M. Solehin and Abu, Mohd. Azlan and Abd. Hamid, Amirah (2021) Investigating performance of transformer health index in machine learning application using dominant features. In: 6th International conference on Advanced Technology and Applied Sciences, ICaTAS 2021, 12 - 14 October 2021, Cairo, Egypt. http://dx.doi.org/10.1088/1742-6596/2128/1/012025
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mohmad, Azlan
Shapiai, M. Ibrahim
Shamsudin, M. Solehin
Abu, Mohd. Azlan
Abd. Hamid, Amirah
Investigating performance of transformer health index in machine learning application using dominant features
description Transformer Health Index (HI) has become a standard tool for performing transformer health evaluations. Due to economic constraints, the recently published paper focuses on developing various techniques to identify the most dominant features for transformer HI prediction. However, the fundamental problems concerning their input features remain unresolved since most suggested features contradict industry practice. In this paper, the primary objective is to investigate the performance of the transformer HI by developing and utilizing only dominant features following the industry recommendation. The investigated dominant features in this paper using 1) CO2/CO ratio and 2) the Incipient fault for detecting temperature abnormalities, and 3) the Dissipation Factor (DF) for detecting oil contamination. The performance validation is carried out using various machine learning (ML) classifiers. Also, the performance of the ML model is validated based on 10-fold type cross-validation to avoid biases in the experiment. As a result, the proposed Artificial Neural Network (ANN) network utilizing the investigated dominant features following the industry practice has produced the highest average accuracy of 80.09% than others ML techniques as a classifier. Hence, additional studies to complement the investigated dominant features may be considered for the subsequent investigation.
format Conference or Workshop Item
author Mohmad, Azlan
Shapiai, M. Ibrahim
Shamsudin, M. Solehin
Abu, Mohd. Azlan
Abd. Hamid, Amirah
author_facet Mohmad, Azlan
Shapiai, M. Ibrahim
Shamsudin, M. Solehin
Abu, Mohd. Azlan
Abd. Hamid, Amirah
author_sort Mohmad, Azlan
title Investigating performance of transformer health index in machine learning application using dominant features
title_short Investigating performance of transformer health index in machine learning application using dominant features
title_full Investigating performance of transformer health index in machine learning application using dominant features
title_fullStr Investigating performance of transformer health index in machine learning application using dominant features
title_full_unstemmed Investigating performance of transformer health index in machine learning application using dominant features
title_sort investigating performance of transformer health index in machine learning application using dominant features
publishDate 2021
url http://eprints.utm.my/id/eprint/96479/1/MohdAzlanAbu2021_InvestigatingPerformanceOfTransformerHealthIndex.pdf
http://eprints.utm.my/id/eprint/96479/
http://dx.doi.org/10.1088/1742-6596/2128/1/012025
_version_ 1739828085809741824
score 13.211869

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