摘要：The temperature of high-voltage cable has a great significance to reflect the operation status, and the accurate prediction of the joint temperature can improve the safe operating level of the wire. This paper points out a temperature prediction model based on least squares support vector machine (LS-SVM) to forecast short-term cable joint temperature. This paper also conducts a test on a Shanghai 110kV cable line with its joint’s history temperature, environmental temperature and humidity, the wire core/sheath current ratio data, and the particle swarm optimization algorithm (PSO) can be adapted to optimize model parameter standardization and regularization parameter dynamically. The results prove that method can predict the temperature of cable joint with high prediction accuracy and also provide a reliable basis for cable temperature detection and early warning system.
