Application of Bayesian networks in composite power system reliability assessment and reliability-based analysis

Bayesian network (BN) is a strong framework for handling probabilistic events which have received limited attention in power system reliability assessment. By applying BN for bulk power system reliability assessment, additional capabilities are provided at modelling and analysis levels in comparison with conventional methods. This study proposes a methodology to apply BNs to composite power system (CPS) reliability modelling, reliability assessment and reliability-based analyses. A minimal cutset (MC)-based method is proposed to extract the BN structure. Moreover, BN parameters are defined based on logical relationships between components, MCs and system failure. In addition, some issues of BN application to large systems are investigated. A variety of reliability-based analyses, useful in different power system studies are introduced and discussed in details which are provided by applying BNs to CPS reliability. The computational efficiency of the presented method is demonstrated by comparing it with state enumeration and Monte Carlo simulation methods. The proposed methodology is implemented on Roy Billinton Test System to extract its BN model based on which analysis issues are considered. Finally, the proposed methodology is applied to the simple, but representative, system the IEEE-reliability test system to show its feasibility in larger systems.