The electrical power grid is one of the most important infrastructures in the modernsociety. It supplies industrial and private customers with electricity and supportsother critical infrastructures such as the water supply. Thus, it is significant that the power grid is a reliable system. However, the power system experiences a hugetransition from classical production methods such as coal and nuclear power plantsto distributed renewable energy forms such as wind energy and photovoltaic. This change to a more distributed system challenges the existing power grid as well as the traditional business models of electric utilities. Thus, cost minimization to increase profitability and improvement of the power grid to increase customer satisfactionare in the focus. One approach to increase the reliability of the grid and decrease maintenance costs is a condition-based maintenance approach which requirescondition monitoring techniques.

This thesis introduces into failure rate modelling for individual power system components and develops a method to calculate individual failure rates based onthe average failure rate, failure statistics, and condition monitoring data. This approach includes the analysis of failure statistics to identify failure causes and failure locations which are population characteristics but can be utilized to describe the heterogeneity within the population. Thus, the thesis first introduces into the topic of failure analysis and heterogeneity in populations. Different factors are identified and categorized which describe the condition development of a component overtime. Then, the literature within failure rate estimation is reviewed to present the factors which are used within failure rate modelling and to outline the existingmethods which consider the individual. However, limitations are discussed which emphasize the demand for a new approach. Consequently, this thesis introduce intoa new approach for estimating the failure rate for individual components.

A set of vital societal functions such as health and safety are necessary for today's society to function and to secure the life of its individuals. Infrastructure is required to provide and maintain these functions. This for society critical infrastructure includes electronic communication technology, transport systems, oil \& gas supply, water supply, and the supply of electric power. The electric power system plays a central role in the critical infrastructure since it is required to operate all others. Therefore, outages in the power system can have severe consequences not solely for the supply of electricity but also for the supply of water, gas, and food. To provide a reliable and safe power supply, power system operators are applying asset management strategies to investigate, plan, maintain, and utilize the system and its components while improving the performance under its own financial constraints.

One approach to increase the reliability of the power grid while decreasing costs is maintenance planning, scheduling, and optimization. To optimize maintenance, a reliability measure for power system components is required. The failure rate, which is the probability of failure in a predefined interval, is utilized in maintenance optimization. Thus far, an average failure rate has been assigned to all components of the same type due to a shortage of component failure data. However, this limits the accuracy of maintenance techniques since the component heterogeneity is neglected. Moreover, the actual failure rate is being underrated or overrated and it is a challenge to identify the impact of conducted maintenance tasks.

This thesis presents how the failure rate accuracy can be improved despite limited failure data available. Firstly, an introduction to failure rate modelling theory, concepts, and definitions is given to provide a common understanding for the later chapters and papers. Secondly, regression models are presented which can be used to model, predict, and characterise the failure rate and failure intensity for power system components. The Cox regression and regression models for count data are applied to two case studies of disconnector and circuit breaker failure data. The results contribute to an improved modelling of the failure rate on individual level but also improve the understanding of risk factor's impact on component failures. However, the aforementioned regression models have rarely been applied in the power system domain due to the limited failure data. Thirdly, the necessity to distinguish between population and individual failure rates is illustrated and risk factors and methods are presented, which are frequently used in failure rate modelling. Moreover, the thesis presents a method to calculate and predict individual failure rates despite the occurrence of actual failures which is of particular advantage for new components.