The reliability assessment of power supply systems is complicated by the branched network topology, redundancy, switching operations, post-failure restoration, operating constraints, and uneven equipment degradation. Additional difficulties arise because calculations based on averaged normative parameters often fail to reflect the actual condition of specific components. This review critically compares reliability assessment methods for power supply systems, with particular attention to the technical condition of electrical equipment. Fault tree analysis and the logic–probabilistic method are discussed as structural approaches. Markov models are examined as tools for describing transitions between equipment states. Monte Carlo-based simulation is considered for calculating adequacy deficits and the operational consequences of supply interruptions. Reliability parameterization based on health indices, correction factors, and consumed life is also analyzed. The analysis indicates that no single methodology is equally effective at all problem levels. The main contribution is to separate technical condition parameterization, structural operability modeling, and adequacy assessment into interacting layers. This structure links condition indicators, such as health indices or consumed life, with system-level reliability and deficit indices through element-state probabilities. The review therefore supports a multi-level framework in which technical condition is considered at the element level, structural reliability is evaluated by the logic–probabilistic method, and failure consequences are assessed by simulation.

This paper presents a methodological approach to assessing structural reliability of power supply systems by incorporating electrical equipment health via the logic-probabilistic method. The implementation of the proposed approach involves establishing specific rules for the construction of reliability block diagrams that capture logical interdependencies of components. Furthermore, a Python-based software tool is developed to automate the reliability assessment of power supply systems. The performance of the developed software was validated using operational data from a real-world 6(10)/0.4 kV power supply system. Specifically, the probability of failure-free operation and the failure probability were calculated based on the electrical equipment health data. To this end, a dedicated database was created to monitor how the thermal stress impacts the equipment service life depletion. The software calculates both the probability of failure-free operation and failure probability for a reliability block diagram based on a specified logical criterion. It also generates time-dependent probability curves of failure-free operation, highlighting the divergence between nominal and health-aware profiles for both the overall system and the specific electrical equipment.