A Robustness Temperature Inversion Method for Cable Straight Joints Based on Improved Sparrow Search Algorithm Optimized BPNN

Abstract

The temperature of cable conductor is of great significance to improve the current carrying capacity, asset utilization and safe operation of cable lines. Aiming at the problems of slow calculation speed, low accuracy and weak anti-interference ability of the current temperature calculation methods, this paper proposes an inversion method based on improved sparrow search algorithm (ISSA) optimized back propagation neural network (BPNN). Tent mapping was used to increase the initial population diversity of sparrow. Modified sparrow optimization formula to improve convergence speed. Chaotic perturbation is applied to the optimal individual to improve the global and local search ability of SSA. The multi-physics simulation model of 110kv straight connector was established, and the temperature distribution data under three different working conditions were obtained. According to the simulation data and CEC2017 standard test experiments, the optimization ability of the improved model is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA), SSA and MSWOA. To verify the generalization performance and migration ability of the proposed method, the thermal cycle test and inversion calculation of the 10kV cable straight-through joint were carried out. The results show that ISSA-BPNN has high accuracy, fast convergence speed, good robustness, and is less affected by cable joint type, load current and cable environment conditions. It has good engineering practicability.

Keywords

• Sparrow search algorithm
• BP neural network
• tent chaos
• cable straight joint
• temperature inversion