Particle swarm optimization trained neural network for overhead line conductor temperature prediction

Transmission system operators often use Static Thermal Rating (STR) for maximum allowable thermal rating of Overhead Line (OHL) conductor. Such static thermal limits are usually defined for operation in extreme weather conditions which are rarely achieved in real-world operation. In this paper, based on the weather parameters collected from an automated weather station installed on a transmission tower, the conductor temperature is estimated using newly developed method based on Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO). Calculated temperatures are compared with measured temperatures from Overhead Transmission Line Monitoring (OTLM) device. Correct estimation of OHL conductor temperature leads to better prediction of Dynamic Thermal Rating (DTR).