Continuous Markov-Based Reliability Analysis of Underground Distribution Power Systems under Temperature and Load-Dependent Failure Rates
Article
Khan, U, Iqbal, H, Roy, S et al. (2025). Continuous Markov-Based Reliability Analysis of Underground Distribution Power Systems under Temperature and Load-Dependent Failure Rates
. 10.1109/ACCESS.2025.3636825
Khan, U, Iqbal, H, Roy, S et al. (2025). Continuous Markov-Based Reliability Analysis of Underground Distribution Power Systems under Temperature and Load-Dependent Failure Rates
. 10.1109/ACCESS.2025.3636825
In metropolitan and densely populated areas, underground distribution is common to enhance operating safety reduce exposure to the elements and for aesthetics. There is limited allowance for heat to escape because of confined in conduits and vaults making major components very susceptible to damage by high temperatures. Conventional reliability assessments largely assume constant failure rates leaving out the effect of temperature stress on equipment performance. A temperature-aware reliability modeling framework for underground distribution components is developed in this study using a continuous-time Markov process (CTMP) approach. Pad mount transformers, metal-clad draw-out circuit breakers, pad mount switches, underground primary cables, and cable splices/joints are examples of components in the underground network being studied. As indicated by the observed data, failure rates were adjusted to reflect degradation behavior under different operating and environmental conditions. The CTMP formulation captures both failure and repair transitions that make it possible to carry out an assessment at the system level for availability and reliability metrics. High temperatures significantly degrade component dependability and overall system performance, as presented in case studies with temperature-dependent modeling predicting probabilities of failure noticeably higher than those resulting from constant-rate models. In fact, with up to 99% higher probability of failure, temperature-dependent modeling reveals that high temperatures significantly degrade component dependability and overall system performance, as evidenced by case studies. The novelty of this study lies in the introduction of a CTMP framework that is a function of both temperature and load. Thus, thermal acceleration behavior is introduced into stochastic reliability analysis which can serve as an even more accurate and practically useful basis for decision-making about network planning and maintenance in underground power networks.
