Previous research carried out under an IFI project suggested that oil regeneration carried out in a window at the end or near the end of a transformers nominal life would extend life by approximately 10 years. The First Tier project deployed online monitoring equipment at five sites where the oil regeneration technique was used.
The NIA project will build on this research by exploring the optimum point to apply oil regeneration to a transformer fleet. It is acknowledged that the life of oil impregnated paper insulation determines the maximum potential life of a transformer, although other factors may cause it to fail earlier. This project scope will aim to determine if mid life oil regeneration can reduce the rate of paper degradation, and thereby further extend the lifespan of the transformer compared to oil regeneration at end of life.
For this project, ten 33kV paired transformers and three 132kV paired transformers (13 sites, 26 transformers) which are at various stages of their design life will be identified.
At each site, only one of the transformers will undergo oil regeneration. Online monitoring equipment will be installed on both transformers at each site to allow comparison of their oil condition and to determine the theoretical life extension over time. These results will be fed into the previously funded data visualisation software to allow consistent comparison.
Electricity North West will work closely with industry experts to validate the data and calibrate the life extension results.
The project will allow Electricity North West to develop its understanding of the effects of life extension on transformer failure modes and maintenance requirements and to identify the optimum window for oil regeneration in the life cycle of transformers.
Benefits
Specification and sourcing of oil regeneration unit capable of delivering the required oil quality in a controlled manner.
Complete oil regeneration and condition monitoring equipment at 13 transformer sites at mid life
Data acquisition, analysis and validation to identify the optimum point of oil regeneration in a transformer life cycle
Learnings
Outcomes
Our previous innovation projects researched, designed, and built an Oil Regeneration unit which ENWL used to regenerate the transformer oil at 13 sites.
Using the online and offline oil sampling a history of results has been created and analysed. The combination of the detailed data into a smaller number of parameters has provided benefit in understanding the information collected and enabling comparison between transformers.
The analysis was used to identify the optimum point of oil regeneration in a transformer life cycle and therefore satisfy the scope of this project.
The project concluded that the optimum time for oil regeneration in 132kV/33kV & 33kV/HV transformers is at the assets mid-life (30-45 years from installation).
The project reconfirmed the benefits of oil regeneration across several asset types and ages and identified a small number of key metrics and values at which oil regeneration can be optimised.
The project also reconfirmed that a major driver to transformer asset life is its electrical loading (heating effect) which can make predictions of future life measured in years difficult.
Lessons Learnt
Several lessons and opportunities for future work and improvement where identified. ENWL are considering if these are best progressed as a future innovation project(s) or as an evolution of business-as-usual systems and operating practice
This learning includes:
· Continued programme of online and offline oil sampling, and continued collation of data.
· Continued analysis of data to extend knowledge over a greater time period to identify any new trends.
· Improving the assessment of the impact of temperature on the speed of ageing, and how information on transformer loading and oil assessments can be combined in a practical manner.
· Amending the current frequency, scope, and quality of current sampling regime to further improve data used for analysis.
· Remaining aware of technology developments that may improve future sampling regimes.
