Transformer life prediction using data from units removed from service and thermal modelling
Understanding how and when transformers are likely to fail is of critical importance to the asset management of large networks. Ideally a reliable end-of-life model would be established based on years of previous experience over several lifecycles of equipment of a type representative of that still...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2010
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1713/ http://eprints.uthm.edu.my/1713/1/A2_212_2010_%2D_Final.pdf |
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| Summary: | Understanding how and when transformers are likely to fail is of critical importance to the asset
management of large networks. Ideally a reliable end-of-life model would be established based on
years of previous experience over several lifecycles of equipment of a type representative of that still
in service. For large power transformers however establishing such a model is not easy because we are
still in the first asset lifecycle and many of the transformers installed when the National Grid system
was first developed are still in service well beyond their original design life.
Maintaining the reliability of the network is clearly a priority, but this must be done efficiently and
economically . Replacement plans must therefore be properly targeted both tactically, to replace those
transformers in the worst condition and at the most critical sites, and strategically to make sure the
replacement numbers are sustainable in terms of available resources such as outage capacity, supplier
capability and investment funding. Given the expected strongly non-linear relationship between
system reliability and plant reliability and the possibility of hidden failures in the system where plant
in service has aged to a condition where it cannot withstand the power flows resulting from a system
failure elsewhere, the best possible understanding of ageing and failure is required.
This paper gives the historical failure rate of the transformers used on the National Grid system in the
UK and shows that failures to date are random in nature and not statistically age related. This means
that traditional approaches to building a statistical end-of-life model cannot be used. Analysis of the
insulation of transformers removed from service for any reason indicates a very wide range of
condition, some samples show severe thermal ageing and it is clear that age-related failures can be
expected if replacement is not carried out, other samples show little ageing and for these transformers
it appears that very long lifetimes might be expected if other ageing mechanisms do not become
apparent.
Transformer condition assessment techniques are now very good and provide a sound basis for
replacing transformers before failure. A condition assessment scoring system based on anticipated
remaining useful lifetime has been implemented and condition based replacement has made a major
contribution to maintaining system reliability. The success of the condition based replacement process
however has a profound biasing effect on the failure statistics as transformers are not left in service to
fail, so that these statistics cannot be used directly for lifetime modelling. |
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