This is the published version of a paper published in Electric power systems research. Citation for the original published paper (version of record):Jürgensen, J H., Nordström, L., Hilber, P. (2016) Individual failure rates for transformers within a population based on diagnostic measures. The high monetary value of a transformer has placed the transformer life-time optimization into the focus of asset management. The average failure rate has created reasonable results within reliability modeling, however, it cannot reflect the probability of failure for an individual transformer. In this paper, a method is introduced to calculate individual failure rates for a transformer population based on failure statistics and diagnostic measurements such as dissolved gas, and 2-furfuraldehyde analysis. The method is applicable to all types of components and the comprehensibility makes it effective for practical implementation. The results are evaluated against two health indices based on a weight factor and fuzzy logic. It can be observed that the presented individual failure rates are plausible representatives of the transformer's probability of failure. Therefore, the results can also be utilized for asset management decision-making.

Over the past decade, the health index has become an increasingly popular asset management tool in utilities. The health index as a condition indicator can improve the decision-making process. However, it also has challenges, which need to be considered during development and implementation. This study addresses the advantages and disadvantages of the health index as a condition indicator in a critical discussion. Moreover, a case study is presented where a health index is calculated for three transmission power transformers. The case study illustrates that age and the load factors included in the health index calculation lead to an immoderately high health index for the transformers T2 and T3. Thus, the study ageing of the transformer windings are used instead which results in a plausible condition representation of all three transformers. The case study also demonstrates that the observation of condition trends over time is lost if the health index is transformed into a linguistic expression.

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On the status of reliability studies involving primary and secondary equipment applied to power system. Abstract-Growth in infrastructure and energy utilization consistently put forward the demand for added quality and quantity of electric power. Reliability concerns over power systems are widespread within its different associated divisions like 'primary' power system structure, protection system, control equipment, ICT (Information and Communication Technologies) etc. This paper is a review of the present status of practices regarding reliability analysis in these divisions and works towards collectively assessing some of the studies in the respective areas. The idea of integrating reliability analysis from the above areas is introduced along with pointing out the major challenges associated. A set of tools for operators to make use in these reliability evaluations and modelling are mentioned. The earlier attempts towards combined overall system reliability analysis are discussed and the approach in this regard with the help of 'control functions' is emphasised. The paper includes works dealing with theory, different methodologies and data associated with power system reliability.

The failure rate is essential in power system reliability assessment and thus far, it has been commonly assumed as constant. This is a basic approach that delivers reasonable results. However, this approach neglects the heterogeneity in component populations, which has a negative impact on the accuracy of the failure rate. This paper proposes a method based on risk functions, which describes the risk behavior of condition measurements over time, to compute individual failure rates within populations. The method is applied to a population of 12 power transformers on transmission level. The computed individual failure rates depict the impact of maintenance and that power transformers with long operation times have a higher failure rate. Moreover, this paper presents a procedure based on the proposed approach to forecast failure rates. Finally, the individual failure rates are calculated over a specified prediction horizon and depicted with a 95% confidence interval.

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