Enhancing transformer protection: A machine learning framework for early fault detection

Alenezi, Mohammed, Anayi, Fatih ORCID: https://orcid.org/0000-0001-8408-7673, Packianather, Michael ORCID: https://orcid.org/0000-0002-9436-8206 and Shouran, Mokhtar 2024. Enhancing transformer protection: A machine learning framework for early fault detection. Sustainability 16 (23) , 10759. 10.3390/su162310759

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Abstract

The reliable operation of power transformers is essential for grid stability, yet existing fault detection methods often suffer from inaccuracies and high false alarm rates. This study introduces a machine learning framework leveraging voltage signals for early fault detection. Simulating diverse fault conditions—including single line-to-ground, line-to-line, turn-to-ground, and turn-to-turn faults—on a laboratory-scale three-phase transformer, we evaluated decision trees, support vector machines, and logistic regression models on a dataset of 6000 samples. Decision trees emerged as the most effective, achieving 99.90% accuracy during 5-fold cross-validation and 95% accuracy on a separate test set of 400 unseen samples. Notably, the framework achieved a low false alarm rate of 0.47% on a separate 6000-sample healthy condition dataset. These results highlight the proposed method’s potential to provide a cost-effective, robust, and scalable solution for enhancing transformer fault detection and advancing grid reliability. This demonstrates the efficacy of voltage-based machine learning for transformer diagnostics, offering a practical and resource-efficient alternative to traditional methods.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	MDPI
ISSN:	2071-1050
Date of First Compliant Deposit:	16 December 2024
Date of Acceptance:	6 December 2024
Last Modified:	17 Dec 2024 10:45
URI:	https://orca.cardiff.ac.uk/id/eprint/174758

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