Continuous-Time Markov Chain based sequential analytical reliability assessment approach for power distribution networks

Reliability assessment plays a crucial role in the planning and operation of power distribution systems. In this paper, a Continuous-Time Markov Chain with Impact-Increment State Enumeration (CTMC-IISE) method is proposed to enable sequential reliability analysis of distribution networks. With the proposed method, the expectation, probabilistic, duration and frequency reliability indices can be obtained considering power flow constrains. To address the computational inefficiency typically associated with CTMC models in large-scale systems, the number of contingency states is significantly reduced through component grouping and state merging. As a result, the proposed approach enhances both the accuracy and efficiency of the reliability assessment process. The effectiveness of the CTMC-IISE method is demonstrated through three case studies: the RBTS Bus 6 system, the IEEE 123-node test feeder, and a practical distribution network in a town. The results highlight the superior performance of the proposed approach compared to Sequential Monte Carlo Simulation (SMCS), traditional CTMC, and Failure Mode and Effects Analysis (FMEA) in terms of computational efficiency and assessment accuracy in distribution system reliability evaluation.