A large power transformer is among the most expensive and critical assets on the grid, and an unexpected failure can cause prolonged outages. Traditional maintenance is either reactive (fix after failure) or scheduled (service on a fixed calendar, often unnecessarily). AI-based predictive maintenance instead watches the transformer's condition continuously and predicts faults before they occur.
Working principle
The key data source is dissolved gas analysis (DGA): insulating oil develops characteristic gases (hydrogen, acetylene, ethylene) when overheating or arcing degrades it. Combined with temperature, load and partial-discharge sensors, this feeds a machine-learning model trained to classify fault type and estimate remaining useful life. Classic DGA rules (Duval triangle) are now augmented or replaced by data-driven models that catch subtle patterns earlier.
| Strategy | Trigger | Drawback |
|---|---|---|
| Reactive | After failure | Outages, collateral damage |
| Preventive (scheduled) | Calendar / hours | Over- or under-maintenance |
| Predictive (AI) | Predicted condition | Needs data & models |
Why it mattersThe value is avoided catastrophic failure and optimised spending: maintenance is done when the asset actually needs it, not too early or too late.
Applications
- Transmission and distribution transformer fleets
- Generator step-up units at power plants
- Industrial and renewable-plant transformers
References & further reading
- Duval, “A review of faults detectable by gas-in-oil analysis in transformers,” IEEE EI Magazine, 2002.
- Wani et al., “Diagnosis of Incipient Faults in Power Transformers using ML,” IEEE Access, 2021.
- IEEE C57.104 / IEC 60599, Guides for the interpretation of dissolved gas analysis.