| Transformer life extension The analysis then combined:
CNAIM-derived probability of failure rates; and future grid development plans.
For every transformer, the calculated replacement year corresponded to whichever criterion reached its limit first — either the projected probability of failure or the allowable thermal loading. This combined assessment offered a more accurate view of RUL than either input alone or traditional age-based replacement method. Figure 3 shows the different replacement scenarios for a selected transformer.
Result: 141 additional years of operation
Under Scenario C (high growth), the analysis showed that the ten monitored transformers could collectively provide 141 additional service years beyond conventional replacement planning. This uplift represents the difference between a traditional age- or rated-load-based retirement decision and a data-driven assessment of transformer condition and dynamic thermal loading limits.
The pilot project highlighted several operational benefits for DSOs: Enhanced asset lifetime. Condition-based insights allow transformers to remain in service longer, deferring capital expenditure and reducing demand on supply chains. Increased network capacity. Accurate thermal assessment enables higher utilisation of existing assets, a critical factor in constrained low-voltage networks.
Improved investment planning. Combining load forecasts and health indices provides a more stable foundation for long-term network development.
The method illustrates a broader trend in distribution networks: the shift from time- based maintenance towards data-driven asset management. By capturing the real operating environment of transformers, DSOs can refine PoF models, adjust loading strategies and align replacement timing with actual need rather than fixed age thresholds. This becomes increasingly important as Europe’s low-voltage transformer population approaches critical age and as electrification accelerates. Digital monitoring offers a means to optimise existing assets while supporting long-term reliability.
Unlocking hidden capacity and extending life
The German pilot project shows how continuous transformer monitoring, paired with established modelling frameworks and long-term planning scenarios, can provide DSOs with a clearer, more actionable understanding of asset health and utilisation. With 141 additional transformer-years identified across only ten units, the potential system-wide impact is substantial. As grid operators seek to balance reliability, cost efficiency and sustainability, real-time monitoring offers a practical pathway to unlock hidden capacity and extend the life of ageing infrastructure. With growing operational complexity across distribution networks, the value of accurate, real-time data will only increase.
Figure 1. Conventional replacement scenarios for low-voltage transformers
Figure 2. Utilisation prognosis for a selected transformer, with respective load limits
Figure 3. Future probability of failure, and replacement scenarios, for a selected transformer
www.modernpowersystems.com | November/December 2025 | 23
Image: Oktogrid
Image: Oktogrid
Image: Oktogrid
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