Deep learning model optimization of 110 kV line ice-melting technology without power failure

Abstract

Abstract In order to achieve ice disaster warning for transmission lines, this article proposes an improved method based on particle swarm optimization (PSO) backpropagation (BP) neural network for ice thickness prediction. The study selects multiple factors such as temperature, humidity, and wind speed, introducing neural network prediction methods to explore the situation of ice thickness on transmission lines. The predictions from this model demonstrate closer alignment with actual values and achieve superior prediction accuracy compared to both the BP model and the PSO-BP model, and the average absolute percentage error of improved PSO (IPSO)-BP is 0.007. Using a 110 kV line as the experimental object, the study conducts ice melting without power outage by changing of the grid currents. Additionally, this predictive model method is employed for ice warnings, assessing ice coverage levels by computing the ice coverage ratio. This facilitates precise control over the activation and deactivation of ice melting devices. The method proposed in this study addresses the issue of low accuracy resulting from the singular data types used in traditional early warning models. Future efforts should focus on further validating the applicability of this method under varying climatic and environmental conditions to achieve real-time, precise control over line ice melting.