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

以夏季高温和高荷载条件下绝缘跳线夹的过热的智能感知为研究对象,建立绝缘跳线夹在典型作业工况下的电-热多物理场耦合三维有限元模型,通过试验验证了模型的有效性,并获取绝缘跳线夹在不同电流负荷、光照强度、环境温度及风速等因素下温度场分布数据作为线夹过热感知模型的训练样本。为了提高麻雀搜索算法(sparrow search algorithm,SSA)在全局搜索的能力引入反向学习策略构建了改进麻雀搜素算法(improved sparrow search algorithm,ISSA),采用改进麻雀算法优化BP神经网络(improved sparrow search algorithm optimization back propagation neural network,ISSA-BPNN)建立绝缘跳线夹温度预测模型,并使用均方值、决定系数评价ISSA-BPNN与粒子群算法优化BP神经网络(particle swarm optimization back propagation neural network,PSO-BPNN)、遗传算法优化BP神经网络(genetic algorithm optimization back propagation neural network,GA-BPNN)、麻雀搜索算法优化BP神经网络(sparrow search algorithm optimization back propagation neural network,SSA-BPNN)及BP神经网络5种算法的预测精度。结果表明,ISSA-BPNN模型相较于其余4种算法的预测模型其预测平均误差可控制在0.71%以内,且收敛速度更快,可以更加精准预测绝缘跳线夹温升,为绝缘跳线夹的状态检测与评估提供了依据。

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.