Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.570251
Title: Power transformer end-of-life modelling : linking statistics with physical ageing
Author: Zhong, Qi
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2012
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Abstract:
Investment decisions on electric power networks have developed to balance network functionality and cost efficiency by analyzing the main risks associated with network operation. Ageing infrastructures, like large power transformers in particular, aggravate the stress of management, because the failure of a power transformer could cause power supply interruption, network reliability reduction, large economic losses and also environment impacts. Transformer asset management is therefore aimed to develop a cost-efficient replacement strategy to get the most usage of transformers. The main objective of this thesis is to understand how UK National Grid transformer assets failure trend can be used, as the engineering evidence to help make financial decisions related to transformer replacements. The studies in this thesis are implemented via two main approaches. First statistical analyses methods are undertaken. This approach is realized to be non-optimal, because the transformer failure mechanism at the normal operation stage is different from that when transformers are aged. Secondly, the transformer physical ageing model is used to estimate thermal lifetimes under the ageing failure mechanism. In conjunction with the random hazard rate obtained by statistical analyses, the actual National Grid transformer population failure hazard with service age is derived. Statistical analyses are carried out based on the ages of National Grid failed and in-service transformers. Transformer lifetime data are fitted into various distribution models by the least square estimator (LSE) and maximum likelihood estimator (MLE). Statistics are however powerless to suggest the population future failure trend due to their intrinsic limitations. National Grid operational experience actually indicates a stable and low value of the random failure hazard rate during the transformer early operation ages. The engineering knowledge however suggests an ageing failure mechanism exists which corresponds to an increasing hazard in the future. Transformer lifetime under ageing failure mechanism is conservatively indicated by its thermal end-of-life corresponding to a specific level of insulation paper mechanical strength. By analyzing National Grid scrapped transformers’ lowest degree of polymerization (DP), these transformers are estimated to have deteriorated at different rates and their thermal lifetimes distribute over a wide age range. The limited number of scrapped transformers cannot adequately indicate the ageing status of the whole population. A transformer’s thermal lifetime is determined by its loading condition, thermal design characteristics and installation site ambient temperature. However, these input data are usually incomplete for an individual transformer.A simplified approach is developed to predict the National Grid in-service transformer’s thermal lifetime by using information from scrapped transformers. The in-service transformer population thermal hazard curve under ageing failure mechanism can thus be obtained.Due to the independent effect from transformer random failure mechanism and ageing failure mechanism, the National Grid transformer population actual failure hazard curve with age is therefore derived as the superposition of the random failure hazard and the thermal hazard. Transformer asset managers are concerned about the knee point age, since aged transformer assets threaten network reliability and the transformer replacement strategy needs to be implemented effectively.
Supervisor: Crossley, Peter Sponsor: Sustainable Power Generation and Supply Programme (SUPERGEN) of UK Engineering and Physical Science Research Council (EPSRC) ; UK National Grid Company
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.570251  DOI: Not available
Keywords: power transformer ; end-of-life modelling ; statistics ; transformer ageing ; asset management
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