A Stochastic Framework for Transmission Line Reliability Assessment Using Poisson Process Modeling

Abstract

Transmission line reliability is essential for secure and continuous power system operation, as outages occur randomly due to aging, environmental stress, and operational conditions. This paper presents a stochastic reliability assessment approach based on Poisson process modeling to represent such random failure and repair behavior. Failure events are modeled using homogeneous and non-homogeneous Poisson processes with a Weibull intensity function, while repair times are described using probabilistic restoration models. Analytical expressions are obtained for key reliability indices, including reliability, mean time to failure, mean time to repair, availability, and loss-of-load probability. Using estimated parameters (β = 1.35, η = 1800 h), the mean time to failure is calculated as 1702 h and the mean time to repair as 12 h, resulting in a steady-state availability of 0.993 for a single transmission line. Results further show that series configurations reduce availability to 0.986, whereas parallel redundancy significantly enhances system performance, achieving availability levels above 0.9999. Statistical validation confirms the suitability of the proposed stochastic model for transmission line reliability evaluation. The framework offers a concise and effective analytical tool for reliability assessment and planning of modern power transmission systems.