Three-State Markov Chain Based Reliability Analysis of Complex Traction Power Supply Systems

The reliability of traction power supply systems (TPSSs) is an important consideration in railway electrification and development of high-speed rail. The state analysis of the whole system and individual components often involves the construction of an appropriate system model. The Markov chain has been widely used to evaluate random processes quantitatively and is suitable for the reliability analysis of complex systems with multiple failure modes. A challenge with the Markov chain approach is the curse of dimensionality and its computational complexity increases exponentially with the number of system components. This paper proposes to use a three-state model and system regionalization based on the specific structural features of the TPSS. The failure probability of TPSS over time is analyzed and a new simplified method to compute the average system availability index is developed to evaluate system reliability. Simulation studies confirm that the proposed approach can derive a rich set of reliability indexes, and the reliability of TPSSs can be effectively evaluated.