基于PSO算法的DTS传感模型的参数辨识与优化
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摘要
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 temperatures 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 and simulation show that,when the spatial resolution is 1 m, the predicted error is within ±25%, and the temperature measurement error of the distributed optical fiber temperature sensing system is no more than ±0.40℃.
Based on fiber Raman scattering effect and Monte-Carlo method, a DTS(distributed optical fiber temperatures 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 and simulation show that,when the spatial resolution is 1 m, the predicted error is within ±25%, and the temperature measurement error of the distributed optical fiber temperature sensing system is no more than ±0.40℃.
引文
[1] 张汝山,吴硕,涂勤昌,等.高空间分辨率分布式光纤测温系统的设计及应用[J].光学仪器,2015,37(1):79—83.Zhang Rushan,Wu Shuo,Tu Qinchang,et al.Design and application of high spatial resolution distributed optical fiber temperature measurement system [J].Optical Instruments,2015,37(1):79—83.
[2] 余向东,张在宣,祝海忠,等.一种应用于分布式光纤拉曼温度传感器的温度补偿电路[J].中国激光,2010,37(6):1440—1444.Yu Xiangdong,Zhang ZaiXun,Zhu Haizhong,et al.A temperature compensating circuit for distributed optical fiber Roman temperature sensor[J].Chinese Journal of Lasers,2010,37(6):1440—1444.
[3] 王剑锋,刘红林,张淑琴,等.基于拉曼光谱散射的新型分布式光纤温度传感器及应用[J].光谱学与光谱分析,2017,33(4):865—870.Wang Jianfeng,Liu Honglin,Zhang Shuqin,et al.New type distributed optical fiber temperature sensor (DTS) based on Raman scattering and its application[J].Spectroscopy and Spectral Analysis,2013,33(4):865—870.
[4] Rosolem J B,Bassan F R,de Freitas D E,et al.Raman DTS based on OTDR improved by using gain-controlled EDFA and pre-shaped simplex code[J].IEEE Sensors J.,2017,17(11):3346—3353.
[5] Bazzo J P,Pipa D R,et al.Improving spatial resolution of Raman DTS using total variation deconvolution[J].IEEE Sensors J.,2016,16(11):4425—4430.
[6] 张彦琴,宁提纲,雷煜卿.分布式单模光纤测温技术改进及应用研究[J].光通信技术,2019,43(01):37—41.Zhang Yanqin,Ning Tigang,Lei Yuqing.Improvement and application of distributed single mode optical fiber temperature measurement technology[J].Optical Communication Technology,2019,43(01):37—41.
[7] 王垚,江毅,马维一.分布式光纤拉曼测温系统中解调算法的优化[J].光学技术,2018,44(6):646—650.Wang Yao,Jiang Yi,Ma Weiyi.Optimization of demodulation algorithm for the distributed optical fiber Raman temperateru measurement system[J].Optical Technique,2018,44(6):646—650.
[8] 张明江,李健,刘毅,等.面向分布式光纤拉曼测温的新型温度解调方法[J].中国激光,2017,44(3):219—226.Zhang Mingjiang,Li Jian,Liu Yi,et al.Temperature demodulation method for distributed fiber Roman temperature measurement[J].Chinese Journal of Lasers,2017,44 (3):219—226.
[9] 刘艳平,叶宗顺,华涛,等.分布式光纤测温系统光纤衰减不一致的修正方法[J].水电自动化与大坝监测,2012,36(6):34—36.Liu Yanping,Ye Zongshun,Hua Tao,et al.Correction method for inconsistent fiber attenuation in distributed optical fiber temperature measurement system[J].Hydropower Automation and Dam Monitoring,2012,36(6):34—36.
[10] 刘耀琴.Monte Carlo法模拟研究偏振光在各种介质中的传播及光学相干断层成像[D].北京化工大学,2011.Liu Yaoqin.Simulation study on polarized light transport in different media with monte carlo method and optical coherence tomography[D].Beijing University Of Chemical Technology,2011.
[11] 孙红娟.基于Monte Carlo方法的磁流体微观结构建模及光学特性分析[D].秦皇岛:东北大学,2013.Sun Hongjuan.Research on the microstructure and optical properties of the magnetic fluid based on the monte carlo method [D].Northeastern University at Qinhuangdao,2013.
[12] 陈瑞麟,万生鹏,贾鹏,等.基于累加平均的分布式光纤拉曼测温系统[J].应用光学,2018,39(4):590—594.Chen Ruilin,Wan Shengpeng,Jia Peng,et al.Distributed fiber Raman temperature measurement system based on cumulative average.[J].Journal of Applied Optics,2018,39(4):34—36.
[13] 郝宇.基于磁流体的电流传感系统的建模与优化[D].燕山大学,2017.Hao Yu.The modeling and optimization of current sensing system based on magnetic fluids [D].Yanshan University,2017.
[14] 罗华.一种惯性权重自适应的粒子群优化算法[J].电子科技,2017,30(3):30—32+36.Luo Hua.A inertia weight adaptive particle swarm optimization algorithm[J].Electronic Science and Technology,2017,30(3):30—32+36.
[15] 马发林,张林,王锦彪,等.粒子群算法的改进及其在优化函数中的应用[J].计算机与数字工程,2017,45(7):1252—1255+1293.Ma Falin,Zhang Lin,Wang Jinbiao,et al.Improvement of particle swarm algorithm and its application in optimization function[J].Computer & Digital Engineering,2017,45(7):1252—1255+1293.
[2] 余向东,张在宣,祝海忠,等.一种应用于分布式光纤拉曼温度传感器的温度补偿电路[J].中国激光,2010,37(6):1440—1444.Yu Xiangdong,Zhang ZaiXun,Zhu Haizhong,et al.A temperature compensating circuit for distributed optical fiber Roman temperature sensor[J].Chinese Journal of Lasers,2010,37(6):1440—1444.
[3] 王剑锋,刘红林,张淑琴,等.基于拉曼光谱散射的新型分布式光纤温度传感器及应用[J].光谱学与光谱分析,2017,33(4):865—870.Wang Jianfeng,Liu Honglin,Zhang Shuqin,et al.New type distributed optical fiber temperature sensor (DTS) based on Raman scattering and its application[J].Spectroscopy and Spectral Analysis,2013,33(4):865—870.
[4] Rosolem J B,Bassan F R,de Freitas D E,et al.Raman DTS based on OTDR improved by using gain-controlled EDFA and pre-shaped simplex code[J].IEEE Sensors J.,2017,17(11):3346—3353.
[5] Bazzo J P,Pipa D R,et al.Improving spatial resolution of Raman DTS using total variation deconvolution[J].IEEE Sensors J.,2016,16(11):4425—4430.
[6] 张彦琴,宁提纲,雷煜卿.分布式单模光纤测温技术改进及应用研究[J].光通信技术,2019,43(01):37—41.Zhang Yanqin,Ning Tigang,Lei Yuqing.Improvement and application of distributed single mode optical fiber temperature measurement technology[J].Optical Communication Technology,2019,43(01):37—41.
[7] 王垚,江毅,马维一.分布式光纤拉曼测温系统中解调算法的优化[J].光学技术,2018,44(6):646—650.Wang Yao,Jiang Yi,Ma Weiyi.Optimization of demodulation algorithm for the distributed optical fiber Raman temperateru measurement system[J].Optical Technique,2018,44(6):646—650.
[8] 张明江,李健,刘毅,等.面向分布式光纤拉曼测温的新型温度解调方法[J].中国激光,2017,44(3):219—226.Zhang Mingjiang,Li Jian,Liu Yi,et al.Temperature demodulation method for distributed fiber Roman temperature measurement[J].Chinese Journal of Lasers,2017,44 (3):219—226.
[9] 刘艳平,叶宗顺,华涛,等.分布式光纤测温系统光纤衰减不一致的修正方法[J].水电自动化与大坝监测,2012,36(6):34—36.Liu Yanping,Ye Zongshun,Hua Tao,et al.Correction method for inconsistent fiber attenuation in distributed optical fiber temperature measurement system[J].Hydropower Automation and Dam Monitoring,2012,36(6):34—36.
[10] 刘耀琴.Monte Carlo法模拟研究偏振光在各种介质中的传播及光学相干断层成像[D].北京化工大学,2011.Liu Yaoqin.Simulation study on polarized light transport in different media with monte carlo method and optical coherence tomography[D].Beijing University Of Chemical Technology,2011.
[11] 孙红娟.基于Monte Carlo方法的磁流体微观结构建模及光学特性分析[D].秦皇岛:东北大学,2013.Sun Hongjuan.Research on the microstructure and optical properties of the magnetic fluid based on the monte carlo method [D].Northeastern University at Qinhuangdao,2013.
[12] 陈瑞麟,万生鹏,贾鹏,等.基于累加平均的分布式光纤拉曼测温系统[J].应用光学,2018,39(4):590—594.Chen Ruilin,Wan Shengpeng,Jia Peng,et al.Distributed fiber Raman temperature measurement system based on cumulative average.[J].Journal of Applied Optics,2018,39(4):34—36.
[13] 郝宇.基于磁流体的电流传感系统的建模与优化[D].燕山大学,2017.Hao Yu.The modeling and optimization of current sensing system based on magnetic fluids [D].Yanshan University,2017.
[14] 罗华.一种惯性权重自适应的粒子群优化算法[J].电子科技,2017,30(3):30—32+36.Luo Hua.A inertia weight adaptive particle swarm optimization algorithm[J].Electronic Science and Technology,2017,30(3):30—32+36.
[15] 马发林,张林,王锦彪,等.粒子群算法的改进及其在优化函数中的应用[J].计算机与数字工程,2017,45(7):1252—1255+1293.Ma Falin,Zhang Lin,Wang Jinbiao,et al.Improvement of particle swarm algorithm and its application in optimization function[J].Computer & Digital Engineering,2017,45(7):1252—1255+1293.