Data-Driven Condition Monitoring and Fault Detection of Power Transformers Using Machine Learning
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Abstract
Reliable transformer operation is critical for minimizing downtime and ensuring power system stability. Dissolved Gas Analysis (DGA) is the most widely used diagnostic tool, yet ratio-based methods such as the Duval Triangle and Key Gas Method often fail when signatures overlap or appear at early fault stages. While machine learning has improved accuracy, models remain vulnerable to noise, imbalance, and overfitting. This paper proposes a CatBoost-based framework that combines statistical and energy features of H₂, CO, C₂H₂, and C₂H₄ gases with engineered ratios to capture complex inter gas dependencies. With tuned hyperparameters, the model achieved 97.6% overall accuracy and strong class-wise performance: 98.9% (Normal), 94.7% (Partial Discharge), 93.9% (Low-Energy Discharge), and 89.0% (Low-Temperature Overheating). Feature importance analysis identified H₂ and gas ratios as key contributors, while training dynamics showed rapid and stable convergence. The results demonstrate robustness, interpretability, and efficiency, highlighting the framework’s potential for real-time transformer fault detection and improved power system resilience.
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Publication Details
- Type of Publication:
- Conference Name: 2025 28th International Conference on Computer and Information Technology (ICCIT)
- Date of Conference: 19/12/2025 - 19/12/2025
- Venue: Long Beach Hotel, Cox's Bazar , Bangladesh
- Organizer: IEEE Bangladesh Section