In this dissertation, Bayesian Network (BN) is used for reliability assessment of composite power systems with emphasis on importance evaluation of system components. A simple approach is presented to construct the BN associated with a given power system. The approach is based on the capability of the BN to learn from data which makes it possible to be applicable to large power systems. The required training data is provided by state sampling using the Monte Carlo simulation. The constructed BN is then used to perform different probabilistic assessments such as ranking the criticality and importance of system components from reliability perspective. The BN is also used to compute the frequency and duration-based indices without time sequential simulation based inferences. The proposed approach provides the possibility of assessing the components importance in view of different load points. The proposed BN framework is also extended to evaluate reliability energy indices and to achieve a more comprehensive BN model, the approach to consider the weather conditions in the BN is also presented.
