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The paper investigates the effect of age related failure of power transformers on the identification of most critical transformer sites for system reliability. The end-of-life failure model of power transformers is modified first to integrate loading conditions effect. The adopted Arrhenius-Weibull probability distribution, which represents the effect of thermal stress on the transformer's end-of-life failure, was compared with the commonly used Gaussian probability distribution model. The sensitivity of results to the uncertainty in model parameters is thoroughly assessed, and acceptable level of uncertainty is determined. The results demonstrated the importance of integration of loading conditions into the failure model. The sensitivity analysis revealed that the identification of critical transformer sites is not significantly affected by the uncertainty in the failure model parameters and that approximate ranges of parameters can be used instead of accurate values without significant, if any, loss in accuracy. The case studies were performed on a realistic transmission test system with 154 power transformers. [ABSTRACT FROM PUBLISHER]

This paper presents a probabilistic framework for making decisions about the replacement of ageing power equipment. The framework involves three steps: first, to identify the most important and critical components of the system for overall system reliability; secondly, to perform Pareto analysis to relate the replacement of the components to the effect on system reliability indices; finally, to determine the optimum scenario for replacement based on a comparison between the cost of unreliability due to deferring the replacement and the saving on reinvestment cost. The proposed approach is illustrated on a meshed test system modeled using U.K. transmission system parameters, a representative transformer age profile and regulatory energy not supplied values. The results demonstrate the feasibility of the framework for application in the area of power system reliability, and show its feasibility for informing replacement decisions. [ABSTRACT FROM PUBLISHER]