SUMMARYIn electrical power transmission and distribution networks power transformers represent a crucial group of assets both in terms of reliability and investments. In order to safeguard the required quality at acceptable costs, decisions must be based on a reliable forecast of future behaviour. The aim of the present study is to develop an integral transformer lifetime model which involves degradation mechanisms for most relevant subsystems, applicable to individual power transformers and transformer populations. In this paper, we present a predictive model for power transformer reliability which involves three essential ingredients: failure statistics, physical understanding of the degradation process, and actual knowledge of the present condition. The model is based on evaluation of existing literature and past experience on degradation mechanisms, failure modes and diagnostic techniques. The model is illustrated for integral reliability of three transformer failure modes, related to the degradation of the transformer winding insulation, of bushings and of tap-changers.

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Abstract:In this paper the concept of an integral transformer lifetime model is presented. The model provides the best possible prediction of future behaviour given the data available. It treats remaining life in terms of future failure probability, thereby giving better support to the decision taking process than a mere remaining life estimate. The core of the model is a generic description of ageing processes, coupled to a probabilistic approach. The approach presented utilizes various techniques to reduce the uncertainty that is inherent to modelling processes with incomplete knowledge of the operational history. One technique couples the process model to externally measurable quantities; another technique involves a sensitivity analysis, which shows what additional input data gives the most efficient way to improve accuracy. We will illustrate the approach by applying it to a wellknown degradation process: thermal degradation of the transformer winding insulation.

Abstract-This paper presents the remaining lifetime calculation of power transformers paper insulation and consequently of power transformers. The calculations are performed based on two models, which are related to the thermal degradation of the cellulose winding paper insulation: the common IEC loading guide and a paper degradation model. The paper insulation model's prediction can be improved by involving data from furfural analysis. The remaining lifetime is extracted from the fault probability (reliability) of the paper insulation. The two models are brought together, to aid the asset manager in the decision making process. A probabilistic approach is used, which can be coupled to analysis in terms of risks, benefits, costs, and availability by the asset manager.

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In this article we discuss a number of practical implications from recent studies on HVDC design concepts for vacuum components, These studies dealt with microwave tube technology. The conclusions, however, are valid for a wide range of components. The goal of this work is to provide a scientific basis for the design of HVDC vacuum components. From a study of breakdown and emission mechanisms, and from the measured insulating performance of many different geometries, we have derived guidelines for the design of for example insulators and cables. It is further shown how conditioning procedures and operating conditions (operating pressure, insulator charging) should be reflected in the design. We will discuss a number of practical implications regarding insulator design, conditioning, vacuum vs. air operation, HV cables in vacuum and potting.

Abstract-The age of the majority of power transformers installed in the western electricity network reaches 30 to 60 years and replacement on short term seems eminent. A technically sound policy concerning the replacement of these assets requires a model that estimates the life expectancies of individual components and from that calculates parameters related to the behavior of a population of assets as a whole. A probabilistic approach is adopted and is applied to thermal degradation of the transformer paper insulation. In this paper, we will focus on the determination of the population reliability from individual reliabilities. These individual reliabilities are based on Arrhenius modeling of paper insulation degradation, including the inherent uncertainty in the parameters involved. A statistical failure model is used to obtain the population reliability figures. The modeling method is demonstrated on two populations of power transformers in The Netherlands to evaluate the different replacement alternatives. Using the model, strategies can be defined to maximize transformer utilization and postpone replacement. The downside is the need to replace the complete fleet in a relatively short time afterwards.