南方电网技术2023，Vol.17Issue(12)：135-144,10.DOI:10.13648/j.cnki.issn1674-0629.2023.12.016
基于ISSA-BPNN算法的配电线路绝缘跳线夹过热感知方法
Insulated Jumper Clamp Overheating Perception Method of Distribution Line Based on ISSA-BPNN Algorithm
摘要
Abstract
The intelligent perception of overheating of insulated jumper clamp under high temperature and high load conditions in summer is taken as the research object,and the three-dimensional finite element model of electrical thermal multi-physical field coupling of insulated jumper clamp under typical working conditions is established.The validity of the model is verified by experi-ments,and the temperature field distribution data under different current load,sunlight intensity,environmental temperature and wind speed factors are obtained as jumper clamp overheating perception model sample.The reverse learning strategy is introduced to improve the ability of sparrow search algorithm(SSA)in global search and improved sparrow search algorithm(ISSA)is established.ISSA optimized BP neural network(ISSA-BPNN)is used to establish the temperature prediction model of insulated jumper clamp,and the prediction accuracies of ISSA-BPNN,particle swarm optimization BP neural network(PSO-BPNN),genetic algorithm optimized BP neural network(GA-BPNN),sparrow search algorithm BP neural network(SSA-BPNN)and BP neural network are evaluated using the mean square value and determination coefficient.The results show that compared with the prediction models of the other four algorithms,the ISSA-BPNN model can control the average prediction error within 0.71%,with higher prediction ac-curacy and faster convergence speed.It can more accurately predict insulated jumper clamp temperature rise,providing a basis for the state detection and evaluation of the insulated jumper clamp.关键词
多物理场/绝缘跳线夹/带电作业/改进麻雀搜索算法/温度预测Key words
multi-physical field/insulated jumper clamp/live working/improved sparrow search algorithm/temperature prediction分类
信息技术与安全科学引用本文复制引用
王南极,吴田,江全才,徐勇,梁加凯,蔡豪..基于ISSA-BPNN算法的配电线路绝缘跳线夹过热感知方法[J].南方电网技术,2023,17(12):135-144,10.基金项目
国家自然科学基金资助项目(51807110). Supported by the National Natural Science Foundation of China(51807110). （51807110）
