A Machine Learning Approach for Distribution Transformer Fault Detection

Jyoti, Oishi; Kibria, Hafsa Binte; Hossain, Md. Munawar

doi:10.1109/icps60393.2023.10428807

2023 10th IEEE International Conference on Power Systems (ICPS) 2023

DOI: 10.1109/icps60393.2023.10428807

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A Machine Learning Approach for Distribution Transformer Fault Detection

Oishi Jyoti,

Hafsa Binte Kibria,

Md. Munawar Hossain

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2024

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Cited by 1 publication

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Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection

Alenezi,

Anayi,

Packianather

et al. 2024

Sustainability

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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.

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Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection

Alenezi,

Anayi,

Packianather

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

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A Machine Learning Approach for Distribution Transformer Fault Detection

Cited by 1 publication

References 16 publications

Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection

Enhancing Transformer Protection: A Machine Learning Framework for Early Fault Detection

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