Power Transformer Condition Assessment and Asset Management

Power transformers represent critical nodes in electrical networks, and their failure can incur cascading service interruptions, safety hazards and substantial financial losses. Condition assessment encompasses both offline and online monitoring techniques to detect incipient faults, estimate remaining service life and inform maintenance planning. Traditional approaches rely on periodic oil sampling, dissolved gas analysis and frequency response analysis, whereas modern strategies integrate fibre-optic and ultrasonic sensors for continuous real-time data acquisition. Health index methodologies aggregate diverse diagnostic parameters—such as moisture, acidity, furan compounds and dielectric strength—into a single metric that tracks transformer ageing and degradation. Advanced analytics, including machine learning and probabilistic modelling, have been introduced to handle data uncertainty, improve prediction accuracy and support risk-based decision frameworks. Asset management programmes leverage these assessments to optimise inspection intervals, schedule repairs, prioritise replacements and allocate budgetary resources. This holistic approach enhances network resilience, reduces unplanned outages and extends transformer lifespan, thereby delivering global benefits in reliability, safety and cost-effectiveness.

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Power Transformer Condition Assessment and Asset Management publication trend

The graph below shows the total number of articles in power transformer condition assessment and asset management across all publications each year (not limited to Nature Index journals).

Technical terms

Condition monitoring: Ongoing observation of transformer health through measurements such as dissolved gas, temperature and vibration to detect early signs of deterioration.

Health index: Composite metric that synthesises multiple diagnostic parameters into a single score reflecting the overall condition of a transformer.

Dissolved gas analysis (DGA): Technique for identifying and quantifying gases generated in transformer oil, used to infer the type and severity of internal faults.

Probabilistic health index: Health index calculated using statistical or Bayesian methods to account for uncertainty and variability in diagnostic data.

Asset intervention: Planned maintenance action—ranging from minor repair to full replacement—triggered by condition assessment to optimise lifecycle costs and reliability.

References

1. Resilient power system planning using probabilistic health indices. International Journal of Electrical Power & Energy Systems (2024).
2. Oil-Immersed Power Transformer Condition Monitoring Methodologies: A Review. Energies (2022).
3. High voltage power transformer condition assessment considering the health index value and its decreasing rate. High Voltage (2021).
4. Artificial Intelligence-Based Power Transformer Health Index for Handling Data Uncertainty. IEEE Access (2021).