Abstract
High capital investment in critical assets, in particular power transformers requires application of incisive diagnostics to determine performance and identify potential issues which may impact long-term operation and maintenance. Asset failure and replacement represent a worst-case scenario which can be avoided. This paper proposes a model for accurate transformer fault diagnosis using artificial intelligence (AI) methods. In recent years, AI models have evolved for fault classification using Dissolved Gas Analysis (DGA) data. In common practice, most DGA data are collated but less often labelled, thus restricting the development of diagnostic models with different learning approaches. To overcome this and related limitations, this paper proposes a new hybrid DGA method for unlabelled data to extract information to build a diagnostic database. The method uses the Rogers Ratio and Duval Triangle Methods to identify early and premature fault onset. The method also supports common utility asset practices to reduce transformer management costs by minimising damage from unnecessary invasive maintenance. The paper provides a comparative study of artificial neural network (ANN), support vector machine (SVM), K-Nearest Neighbour (KNN) and decision tree models using the constructed database. Comparative results indicate a high level of diagnostic precision with approximately 99% accuracy for classification using a KNN-based model.
Original language | English |
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Title of host publication | The 16th IET International Conference on AC and DC Power Transmission (ACDC 2020): proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1474-1479 |
Number of pages | 6 |
Publication status | Published - 15 Sept 2021 |
Event | 16th IET International Conference on AC and DC Power Transmission, ACDC 2020 - Virtual, Online Duration: 02 Jul 2020 → 03 Jul 2020 |
Conference
Conference | 16th IET International Conference on AC and DC Power Transmission, ACDC 2020 |
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City | Virtual, Online |
Period | 02/07/2020 → 03/07/2020 |
Bibliographical note
Funding Information:The authors would like to express their gratitude to General Electric for collecting and providing DGA data.
Publisher Copyright:
© 2020 Institution of Engineering and Technology. All rights reserved.
Keywords
- Artificial intelligence model
- Dissolved gas analysis
- Duval triangle method
- Rogers ratio
ASJC Scopus subject areas
- Electrical and Electronic Engineering
Fingerprint
Dive into the research topics of 'Artificial intelligence model for transformer fault diagnosis using a constructed database'. Together they form a unique fingerprint.Student theses
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Machine learning for power transformer diagnostics
Wang, L. (Author), Littler, T. (Supervisor) & Liu, X. (Supervisor), Dec 2023Student thesis: Doctoral Thesis › Doctor of Philosophy