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.

In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden.

This paper utilises the proportional hazard model to understand and quantify the impact of explanatory variables on the failure rate of circuit breakers (CB). Particularly, 4496 work orders with 2622 high voltage CBs are investigated with an occurrence of 281 major failures. Different explanatory variables such as CB type, manufacturer, preventive maintenance (PM), and others are gathered to quantify their significance and magnitude of their effect. The results present that PM has a positive impact, the number of operations within the last year a negative impact, and age has a small but negative impact on the failure rate. The CB type is not significant in all analyses which can be explained by examining the PM and age of these CB types. This paper contributes to the understanding of how explantatory variables impact the failure rate which is essential for power system asset management.

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 behaviour 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, the 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.

This paper presents the impact of different explanatory variables such as remote control availability and conducted preventive maintenance, among others, on failure statistics of a disconnector population in Sweden using the proportional hazard model. To do so, 2191 work orders were analysed which included 1626 disconnectors and 278 major failures. Here, the results show that the remote control availability for disconnectors - an example of such Smart Grid technology - has a negative effect on the failure rate, whereas preventive maintenance has a positive impact. It is also shown that the disconnector age is not significant and that certain disconnector types have a significant and positive correlation towards failures when compared to other disconnector types. The results increase the understanding of disconnector failures to improve asset management.

High voltage circuit breaker (CB) are of fundamental importance to protect and operate the power system. To improve their performance and to better predict failures, it is necessary to understand the effect of covariates such as preventive maintenance, age, voltage level, and the CB type. A straightforward approach is to investigate recurrent failures with regression models for count data. In this paper, several regression models are developed to estimate the impact of the aforementioned covariates to predict the recurrence of failures. The results show that age has a significant and negative impact, preventive maintenance before the first failure has a positive impact, and that the voltage level has a negative impact. Moreover, the Poisson, Negative Binomial, and zero-inflated models are compared. The comparison shows that the Negative Binomial model has the best fit to the studied recurrent failure data.

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 paper 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 factor included in the health index calculation lead to an immoderately high health index for the transformers T2 and T3. Thus, the paper 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.

Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) have the potential to increase the power density in power electronics converters compared to the currently used silicon (Si). Their benefits are higher efficiency, higher switching speeds, and higher operating temperatures. Moreover, SiC MOSFETs, which are normally-off, offer the possibility to directly replace Si Isolated-Gate-BipolarTransistors (IGBTs) in already existing converter designs with minimal circuit changes. Nevertheless, as an emerging technology, the reliability performance remains to be investigated. A reliability analysis has been performed based on a full-bridge resonant converter rated at 60 kW for modern Electrostatic Precipitator (ESP) power supplies. This analysis shows that introducing SiC devices will increase the lifetime of the converter while reducing the losses. The investment costs of replacing the Si IGBTs with SiC MOSFETs can thus be covered with the reduction of the losses over the economical operational lifetime. Furthermore, a theoretical analysis on how introducing SiC MOSFETs could increase the power density of the converter while maintaining the efficiency and the reliability. Finally, an analysis on introducing redundancy as a way to improve the reliability of the system has been performed.

Failure rate estimation is an important tool for planning and operating decision making in asset management of the power system. Moreover, the knowledge of how different explanatory variables impact the failure rate of the power system equipment is crucial for substation design. This study investigates 2191 work orders of 1626 non-current breaking disconnectors with 344 major failures. In particular, this paper analyses the disconnector failure data regarding recurrent failure data. Since the original PHM cannot handle recurrent event data, different extensions were developed such as the Andersen-Gill (AG), Prentice, Williams and Peterson (PWP), and the Wei, Lin, and Weissfeld (WLW) model. These models are applied to the disconnector dataset with 140 recurrent time-to-failure processes. The explanatory variables age at admission, remote control, preventive maintenance, and voltage level are assessed. The results show that preventive maintenance has a significant and positive impact on the recurrences with all tested methods. Also remote control, voltage level, and age are significant covariates. Compared to the single failure study previously conducted, where age had no significance, age is significant when assessing the recurrent failure which is the most critical difference to the analysis without recurrences.

The failure rate is a reliability measure which isused for planning and operation of the power system. Thus far, average or experience based failure rates were applied to power system equipment due to their straightforward implementation. However, this approach limits the accuracy of the gained resultsand neglects the important differentiation between populationand individual failure rates. Hence, this paper discusses and demonstrates the necessity to distinguish between populationand individual failure rates and reviews the existing literature offailure rate estimation within the power system domain. The literature is categorized into statistical data driven approaches and failure rate modelling with focus on different criteria whichcan be used to describe the heterogeneity within populations. The review reveals that the environmental impact was modelled predominantly.