基于PSO算法的DTS传感模型的参数辨识与优化

Parameter identification and optimization of the DTS sensing model based on the particle swarm optimization algorithm

基于光纤拉曼散射效应和Monte-Carlo法,建立了一种分布式光纤拉曼温度传感系统(DTS)传感模型。应用改进的PSO算法对所建立的传感模型进行参数辨识,分析了种群数目、迭代次数、惯性权重、加速度因子等参数选值对算法的影响,选取了最佳参数组合。搭建了分布式光纤温度传感系统实验平台,运用所建立的DTS传感模型对分布式光纤温度传感系统进行相关的仿真及预测。实验及仿真结果表明,传感模型在空间分辨率为1m时,预测误差≤±0.25%;该分布式光纤温度传感系统测温误差≤±0.40℃。

Based on fiber Raman scattering effect and Monte-Carlo method, a DTS(distributed optical fiber tempera tures sensing) sensing model was established. Using improved PSO algorithm , the parameter identification of the DTS sensing model is carried out.The influence of parameters such as population number, number of iterations, inertia weight and acceleration factor on operating results of algorithm are analyzed, and the optimal parameter combination was selected.The experimental platform of distributed optical fiber temperature sensing system is set up, and the distributed optical fiber temperature sensing system was simulated and predicted using the DTS sensing model. The results of experiment andsimulationshowthat ,whenthespatialresolutionis1m,thepredictederroriswithin ±25%,and the temperature measurement error of the distributed optical fiber temperature sensing system is no more than ±0.40℃.