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Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques

Power transformer is one of the main components in any power transmission and distribution network. Transformers have complicated winding structures and are subjected to electrical, thermal and mechanical stresses. During the last few years, there has been a trend of continuous increasing of transfo...

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Main Author: Dheyaa, Ghaith Anmar
Format: Thesis
Language:English
English
Published: 2019
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utem.edu.my/id/eprint/24945/1/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf
http://eprints.utem.edu.my/id/eprint/24945/2/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf
http://eprints.utem.edu.my/id/eprint/24945/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117709
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spelling my.utem.eprints.249452023-07-03T15:21:05Z http://eprints.utem.edu.my/id/eprint/24945/ Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques Dheyaa, Ghaith Anmar T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Power transformer is one of the main components in any power transmission and distribution network. Transformers have complicated winding structures and are subjected to electrical, thermal and mechanical stresses. During the last few years, there has been a trend of continuous increasing of transformer failures. It is therefore vital to diagnose the incipient faults for safety and reliability of an electrical network. Thus, these transformers are needed to be routinely maintained. Due to the large number of transformers with different manufactures and capacities, routine maintenance and diagnosis of such transformers are difficult since different transformers exhibit different characteristics and problems. By means of dissolved gas analysis (DGA), it is possible to distinguish faults such as partial discharge (corona), overheating (pyrolysis) and arcing in oil-filled equipment. Dissolved gas analysis is one of the most effective tools for power transformer condition monitoring. There are several interpretation techniques for DGA results including Key Gas, Doernenburg, IEC Ratio, Roger’s Ratio and Duval Triangle. However, DGA interpretation is still a challenge issue as most of the techniques are relying on personnel experience more than standard mathematical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this thesis proposes a new Artificial Intelligence (AI) approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating the Duval triangle method (DTM) with two machine learning classifiers named decision tree (DT) and random forest (RF), which the final interpretation will apply the voting combination method to get the final prediction of the incipient fault inside the power transformer. DGA results of oil samples where the real fault already known that were collected from different published papers were used to train and test the classifiers. The results demonstrate that combining the conventional method with artificial intelligence based on DGA interpretation methods gives reliable diagnosis of the incipient fault in the power transformer. 2019 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/24945/1/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf text en http://eprints.utem.edu.my/id/eprint/24945/2/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf Dheyaa, Ghaith Anmar (2019) Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques. Masters thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117709
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Dheyaa, Ghaith Anmar
Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
description Power transformer is one of the main components in any power transmission and distribution network. Transformers have complicated winding structures and are subjected to electrical, thermal and mechanical stresses. During the last few years, there has been a trend of continuous increasing of transformer failures. It is therefore vital to diagnose the incipient faults for safety and reliability of an electrical network. Thus, these transformers are needed to be routinely maintained. Due to the large number of transformers with different manufactures and capacities, routine maintenance and diagnosis of such transformers are difficult since different transformers exhibit different characteristics and problems. By means of dissolved gas analysis (DGA), it is possible to distinguish faults such as partial discharge (corona), overheating (pyrolysis) and arcing in oil-filled equipment. Dissolved gas analysis is one of the most effective tools for power transformer condition monitoring. There are several interpretation techniques for DGA results including Key Gas, Doernenburg, IEC Ratio, Roger’s Ratio and Duval Triangle. However, DGA interpretation is still a challenge issue as most of the techniques are relying on personnel experience more than standard mathematical formulation. As a result, various interpretation techniques do not necessarily lead to the same conclusion for the same oil sample. Furthermore, significant number of DGA results fall outside the proposed codes of the current based-ratio interpretation techniques and cannot be diagnosed by these methods. Moreover, ratio methods fail to diagnose multiple fault conditions due to the mixing up of produced gases. To overcome these limitations, this thesis proposes a new Artificial Intelligence (AI) approach to reduce dependency on expert personnel and to aid in standardizing DGA interpretation techniques. The approach relies on incorporating the Duval triangle method (DTM) with two machine learning classifiers named decision tree (DT) and random forest (RF), which the final interpretation will apply the voting combination method to get the final prediction of the incipient fault inside the power transformer. DGA results of oil samples where the real fault already known that were collected from different published papers were used to train and test the classifiers. The results demonstrate that combining the conventional method with artificial intelligence based on DGA interpretation methods gives reliable diagnosis of the incipient fault in the power transformer.
format Thesis
author Dheyaa, Ghaith Anmar
author_facet Dheyaa, Ghaith Anmar
author_sort Dheyaa, Ghaith Anmar
title Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
title_short Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
title_full Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
title_fullStr Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
title_full_unstemmed Improvement of dissolved gas analysis interpretation by combining Duval triangle method with machine learning techniques
title_sort improvement of dissolved gas analysis interpretation by combining duval triangle method with machine learning techniques
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24945/1/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf
http://eprints.utem.edu.my/id/eprint/24945/2/Improvement%20Of%20Dissolved%20Gas%20Analysis%20Interpretation%20By%20Combining%20Duval%20Triangle%20Method%20With%20Machine%20Learning%20Techniques.pdf
http://eprints.utem.edu.my/id/eprint/24945/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117709
_version_ 1770555170196815872
score 13.211869

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