\section{Conclusion}\label{sec:Conclusion}

This paper proposed a methodology for integrating HVCB condition data into power system reliability analysis. The data were analyzed to identify the onset of failure mechanisms. The Cox PHM was used to translate the HVCB health indices into condition parameters, and the reliability indices were quantified using approximate methods. The condition of the HVCBs was specified through varying two condition-dependent reliability parameters. One condition parameter was associated with the HVCB tripping without a command \( \lambda^s \), and the other condition parameter was related to the probability of an HVCB failing to trip on command \( P^m \).

It was found that the reliability parameters \( \lambda^s \) and \( P^m \) for an individual HVCB depends significantly on the technical condition of the component. This conclusion holds irrespective of which power system the HVCBs are part of. Using a test system, it was moreover shown how including HVCB condition data can have a negative impact on the reliability of supply indices, and not including the condition may lead to an underestimation of the system reliability indices. The magnitude of the system-level impacts depends on the system and can be relatively small if the reliability of supply indices of the system are dominated by single-outage contingencies. The reliability indices are most affected by the parameter \( P^m \) due to the common-mode failures that it induces and thus its influence on the system-level reliability contribution of multiple-outage contingencies. Therefore, it is important to model the failure mechanisms related to HVCBs failing to trip on command accurately.

Future work could focus on incorporating more types of condition data, such as operation and maintenance records, into the health index model to assess more failure mechanisms. Also, using a time-sequential Monte Carlo simulation methodology would enable the distribution of the reliability indices to be investigated.