A probabilistic estimation for dynamic thermal rating of transmission lines

Abstract

Dynamic Thermal Line Rating (DTLR) provides actual current-carrying capacity of transmission lines by considering weather conditions perceived to be influential on line thermal capacity. These weather variables include ambient temperature, wind speed, and wind direction. In this paper, a probability technique is adopted to effectively model the existing uncertainties in weather variables. A probability distribution is assigned to each variable to account for the inherent uncertainties in weather data. Monte Carlo Simulation (MCS) technique is then employed to generate different scenarios for relevant weather data. Output of MCS is fed to the IEEE model to obtain the probability distribution of sectional line ampacity estimated at each line span. Next, probability distribution of line ampacity will be calculated as the minimum of the ampacities estimated at each line span. Expected value and related percentiles of line ampacity can be derived from its probability distribution. The proposed approach would enable system operators to make decisions on DTLR accounting for risk and degree of uncertainty that power utilities are willing to accept.