基于非线性遗传算法的DTS传感模型的参数辨识与优化

基于光纤拉曼散射效应和Monte-Carlo法,建立了一种DTS传感模型。采用非线性遗传算法对该模型的参数进行了辨识,分析了种群规模、进化代数、交叉率、变异率等参数选值对算法运行结果的影响,并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,并运用该DTS传感模型对分布式光纤温度传感系统进行了仿真预测。实验及仿真结果表明:该传感模型在空间分辨力为1m时,预测误差在0.24%以内;该分布式光纤温度传感系统的测温误差不大于0.40℃。

Parameter Identification and Optimization of the DTS Sensing Model Based on the Nonlinear Genetic Algorithm

Based on fiber Raman scattering efficiency and Monte-Carlo method, a DTS sensing model is established. The nonlinear genetic algorithm is adopted to conduct the parameters identification of DTS sensing model, and the influence of the population size, evolutionary algebra, crossover rate and mutation rate on the operation result of the algorithm are analyzed,and the optimal parameter combination is selected. The experimental platform of distributed optical fiber temperature sensing system is established, and the DTS sensing model is used to conduct the simulation predicted of distributed optical fiber temperature sensing system. The results of the experiment and simulation show that the predictive error of the sensing model is within 0.24% when the spatial resolution is 1 m, and the temperature measurement error of the distributed optical fiber temperature sensing system is no more than 0.40 ℃.