Lifetime prediction and estimation of power transformer
This project presents a method to estimate and predict failure probability related to aging transformer units in power system. Statistically, the sample mean or the average age method is acceptable if it is used in a case where there is a big population. This method obviously is not suitable f...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2134/1/24p%20SYAHIRA%20RAIHAN%20OON.pdf http://eprints.uthm.edu.my/2134/2/SYAHIRA%20RAIHAN%20OON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2134/3/SYAHIRA%20RAIHAN%20OON%20WATERMARK.pdf |
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| Summary: | This project presents a method to estimate and predict failure probability related to
aging transformer units in power system. Statistically, the sample mean or the
average age method is acceptable if it is used in a case where there is a big
population. This method obviously is not suitable for power system components with
very few samples of end-of-life failures. The essential weakness of the sample mean
is that it only uses information of died components. This research proposed an
approach to estimate and predict the lifetime of a power transformer by using NGC
data. The data with both died and survive transformers will make contribution on
estimating the mean life of power transformer. Two methods that used are normal
and Weibull distributions. Although the two methods have different estimation
approaches and solution techniques, they are related to each other and use the same
format of raw data. From this research, the mean life and standard deviation for
normal and Weibull distribution estimation should be quite close and also to the
shape of the both distribution. Thus, statistical reliability analysis can provide
predictions, such percentage of transformer that will fail at a particular time of before
a particular age and how many transformers will fail in the next future year by using
failure rate model. From that prediction, the forecasted capital expenditure ( the cost
of replacement and consequential failure cost) also can be specified. Thus, it will
avoid asset harvesting and the possibility of having unforeseen costs. |
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