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Probabilistic Methodology for Prioritising Replacement of Ageing Power Transformers Based on Reliability Assessment of Transmission System
[Thesis]. Manchester, UK: The University of Manchester; 2014.
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Abstract
Customers expect electricity to be not only available but also affordable whenever they need it. Due to the stochastic nature of power system component failure, the management of power interruption is challenging. Although the reliability of supply can usually be increased by employing redundant equipment; this means that affordability is compromised. At present, many power utilities have a considerable amount of aged equipment in their networks. Although they have already started replacement planning, the price control schemes imposed by regulatory authorities constrain their capital expenditure budget. This thesis has studied the influence of the end-of-life failure of power transformers on transmission system reliability in order to make decisions on their replacement. Power transformers are selected for the analysis because they are technically complex, expensive, and main feed points of electricity for end users. In addition, surveys on ageing asset show that 50% of transformer populations, in many utilities, have been classified as old since the year 2008. The focus of these reliability analyses is to identify the most critical transformers and to establish a reliability based replacement framework. Modelling of end-of-life failure was reviewed, and the state-of-the-art method of its incorporation into system reliability was adopted. A reliability assessment tool within DIgSILENT PowerFactory package was developed in order to perform reliability studies.This thesis has four original contributions surrounding transmission system reliability analysis. The first contribution is the development of a cost-effective framework that concerns the application of reliability studies on asset replacement decision making. The developed framework has employed reliability importance measures, the Pareto analysis and economic comparison based on reliability incentive/penalty schemes. All the three elements of the framework are original applications to system reliability area. The second contribution is the integration of unconventional end-of-life failure models into system reliability. The unconventional model used in this study is Arrhenius-Weibull distribution, which characterises end-of-life failure under different loading conditions. This study has evaluated the added value provided by including loading levels in failure models and how this enhances the understanding of the effect of operational factors on system reliability. The third contribution is the investigation of dependent failure of power transformers caused by thermal stress. This investigation has led to the development of two probabilistic indicators to rank power transformer based on their criticality to multiple failure events. These new indicators have related the transformer reliability to its age and loading levels. In the fourth contribution, comprehensive studies of the effect of uncertainty associated with failure model parameters were performed. The first study has established bases for a system related approach for refining failure models. The approach is based on assessing the sensitivity of the system reliability or the system reliability applications to the uncertainty in failure model parameters. In the second study, two quantification methods were adopted to propagate the uncertainty in failure model parameters to system reliability indices. These are the second order probability and evidence theory. The last uncertainty study has described the use of sampling based sensitivity analysis to identify the most critical transformers and their area of vulnerability. Studies throughout the thesis have been performed on a realistic transmission network and the IEEE Reliability Test System.