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The Study On Reliability And Risk Assessment Of220kV And Above Power Transformers

Posted on:2015-05-28Degree:MasterType:Thesis
Country:ChinaCandidate:E J PangFull Text:PDF
GTID:2272330434457751Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
The current transformer data collection for the transformer assessment at homeand abroad is relatively simple, and some of the features are assessed by the use of the"to be or not to be" system, which means only two states,"qualified" and"unqualified". There is no quantification of indicators, which could not reflect the realinsulation condition effectively, so the transformer condition assessment is notdetailed enough, which is not conducive to grasp the real operational status of thetransformer.This paper carried out further research work for the power transformer conditionassessment, relies on Yunnan Electric Power Research Institute, the reliabilityevaluation method is applied to assess the reliability of power transformers, and atransformer fault tree model is established by using failure modes and effects analysis,to calculate the reliability of the transformer. BP neural network is used to fit thereliability on the basis, using genetic algorithms (GA) to optimize initial weights andthresholds for the BP neural network fatherly, to improve the reliability calculationspeed and accuracy. Secondly, the method is compared with the risk assessmentmethod from the theoretical and practical operation results, to find their strengths andweaknesses. Thirdly, the source data for assessment were analyzed to calculate thecontribution, which means the individual characteristics flection on the results of theassessment. Through the above research, the data was collected based on thecontribution of the feature amount during the assessment of the transformer; thetransformer operating condition is obtained, the reliability of the evaluation rate isimproved, providing the basis for the state maintenance.
Keywords/Search Tags:the power transformer, the reliability assessment, the risk assessment, theBP neural network, contribution
PDF Full Text Request
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