Step response modeling of calibrated furnace temperature and its MCPPID realtime control

doi:10.19781/j.issn.16739140.2020.06.025

Home > Archive>Volume 35, Issue 6, 2020 >187-188. DOI:10.19781/j.issn.16739140.2020.06.025

Step response modeling of calibrated furnace temperature and its MCPPID realtime control
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                        10.19781/j.issn.16739140.2020.06.025
                    
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                        YANG FanYANG Fan
Automation Engineering,Shanghai University of Electric Power, Shanghai 200090 , China ；Shanghai Municipal EngineeringDesign and Research Institute (Group) Co., Ltd., Shanghai 200092 ,China
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YANG PingYANG Ping
Automation Engineering,Shanghai University of Electric Power, Shanghai 200090 , China
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PENG DaogangPENG Daogang
Automation Engineering,Shanghai University of Electric Power, Shanghai 200090 , China
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Clc Number:TP273
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Abstract:

The new MCPPID control technology is often used to improve the calibrated furnace temperature control. In order to improve the control effect, this paper firstly adopts the PSObased step response test modeling method to model the calibrated furnace temperatureand then uses MCPPID technology to control the model. The realtime control test carried out on the automatic temperature control test device of the tube calibrated furnace shows that the PSObased step response test modeling method is effective.Compared with the traditional ZNPID, the MCPPID control has the advantage of small overshoot. Results show that the PID realtime control program developed on deploytool software is successful.

Key words:PSO;step response;calibrated furnace;MCP transfer function;PID control

Reference

[1] 杨平,余洁,孙宇贞.多容惯性标准传递函数的建立与比较[J].上海电力学院学报,2011,27(5):512514+518.YANG Ping,YU Jie,SUN Yuzhen,et al.Establishment and comparison of a multiple capacity process transfer function standard form[J].Journal of Shanghai University of Electric Power,2011,27(5):512514+518.

[2] 杨平,陈式跃.无超调不限阶数的M型次多容惯性标准传递函数[J].中国测试,2013,39(2):1418.YANG Ping,CHEN Shiyue.Mtype multiple capacity process transfer function standard form with noneovershoot and none order limit characteristics[J].China Measurement & Test,2013,39(2):1418.

[3] 杨平,黄伟,孙宇贞,等.PID参数整定的MCP标准传递函数法公式[J].上海电力学院学报,2014,30(1):4045+70.YANG Ping,HUANG Wei,SUN Yuzhen,et al.PID parameters tuning formulae derived from the multiple capacity process transfer function standard form[J].Journal of Shanghai University of Electric Power,2014,30(1):4045+70.

[4] 方文阳,杨平.直流电机调速的MCPPID控制[J].机电产品开发与创新,2015,28(5):9799+123.FANG Wenyang,YANG Ping.MCPPID control for a DC motors speed regulation system[J].Development & Innovation of Machinery & Electrical Products,2015,28(5):9799+123.

[5] 李昀,杨平,贺帅鹏.恒压供水系统的MCPPID控制[J].自动化与信息工程,2013,34(5):1924.LI Yun,YANG Ping,HE Shuaipeng.MCPPID control of pressure for watersupply system[J].Automation & Information Engineering,2013,34(5):1924.

[6] 丁远扬,杨平.基于MCPPID控制器的电站锅炉汽温控制[J].电力科学与工程,2014,30(11):16.DING Yuanyang,YANG Ping.Superheated steam temperature control system of a power plantwith the MCPPID controller[J].Electric Power Science and Engineering,2014,30(11):16.

[7] 靳其兵,刘子宜.基于阶跃响应的带纯滞后闭环辨识新方法[J].系统仿真学报,2010,22(9):21682172.JIN Qibing,LIU Ziyi.Novel identification method with time delay from step response[J].Journal of System Simulation,2010,22(9):21682172.

[8] 徐小平,钱富才,刘丁,等.基于PSO算法的系统辨识方法[J].系统仿真学报,2008,20(13):35253528.XU Xiaoping,QIAN Fucai,LIU Ding,et al.Method of system identification based on PSO algorithm[J].Journal of System Simulation,2008,20(13):35253528.

[9] 牛海明,于佼,丁常富,等.基于多目标粒子群算法的过热汽温自抗扰控制[J].中国电力,2020,53(3):126133.NIU Haiming,YU Jiao,DING Changfu,et al.Active disturbance rejection control over superheated steam temperature based on multiobjective particle swarm optimization[J].Electric Power,2020,53(3):126133.

[10] 徐银凤,罗日成,易生,等.带混沌搜索的PSO算法在变压器局部放电定位中的应用[J].电力科学与技术学报,2010,25(1):97100.XU Yinfeng,LUO Richeng,YI Sheng,et al.Chaos search based PSO algorithm application in transformer partial discharge location[J].Journal of Electric Power Science and Technology,2010,25(1):97100.

[11] 张海燕,王东,樊平,等.基于加权Voronoi图和GAPSO的电动汽车充电站最优规划[J].电网与清洁能源,2019,35(5):7782.ZHANG Haiyan,WANG Dong,FAN Pin,et al.Optimal planning of electric vehicle charging stations based on weighted voronoi diagram and GAPSO[J].Power System and Clean Energy,2019,35(5):7782.

[12] 孟毅男,张健,赵玉文,等.热电偶、热电阻全自动检定装置[J].电测与仪表,2000,37(5):4043.MENG Yinan,ZHANG Jian,ZHAO Yuwen,et al.Automatic calibrator for thermocouple and thermal resistance[J].Electrical Measurement & Instrumentation,2000,37(5):4043.

[13] 马一鸣,周理兵,王晋,等.基于直流阶跃电压试验与NSGA Ⅱ算法的同步电机动态参数辨识方法[J].电工技术学报,2019,34(23):48904900.MA Yiming,ZHOU Libing,WANG Jin,et al.A method for synchronous machine dynamic parameters identification based on DC step voltage test and NSGA Ⅱ algorithm[J].Transactions of China Electrotechnical Society,2019,34(23):48904900.

[14] 李强,史元浩,曾建潮,等.基于PSOElman神经网络的燃煤机组受热面清洁状态预测[J].中国电力,2019,52(5):4853.LI Qiang,SHI Yuanhao,ZENG Jianchao,et al.Forecast of heating surface cleanliness of coalfired power plants based on PSOElman neural network[J].Electric Power,2019,52(5):4853.

[15] Tuppadung Y,Kurutach W.Comparing nonlinear inertia weights and constriction factors in particle swarm optimization[J].International Journal of Knowledgebased and Intelligent Engineering,Systems,2011,15(2):6570.

[16] 黄新波,王享,田毅,等.基于PSOELM融合动态加权AdaBoost的变压器故障诊断方法[J].高压电器,2020,56(5):3946.HUANG Xinbo,WANG Xiang,TIAN Yi,et al.Transformer fault diagnosis algorithm based on PSOELM fusion dynamically weighted AdaBoost[J].High Voltage Apparatus,2020,56(5):3946.

[17] 王俊伟,汪定伟.粒子群算法中惯性权重的实验与分析[J].系统工程学报,2005,20(2):194198.WANG Junwei,WANG Dingwei.Experiments and analysis on inertia weight in particle swarm optimization [J].Journal of Systems Engineering,2005,20(2):194198.

[18] 罗荣慧,马志斌.遗传算法在用户感知评估建模中的应用[J].中兴通讯技术,2014(2):4952.LUO Ronghui,MA Zhibin.Genetic algorithms application in customer experience evaluation model[J].ZTE Technology Journal,2014(2):4952.

[19] 刘伟明.热电偶检定炉的程序控温[J].计量技术,1990(12):3032.LIU Weiming.Program temperature control of thermocouple verification furnace[J].Measurement Technique,1990(12):3032.

[20] 薛光辉,柴敬轩.热电偶传感器温控系统误差研究[J].中国测试,2019,45(9):100104.XUE Guanghui,CHAI Jingxuan.Temperature control error research based on thermocouple sensor[J].China Measurement & Test,2019,45(9):100104.

[21] 杨平,邓亮,徐春梅.PID控制器参数整定方法及应用[M].北京:中国电力出版社,2016.

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杨帆,杨平,彭道刚.检定炉炉温的阶跃响应试验建模与MCPPID实时控制[J].电力科学与技术学报英文版,2020,35(6):187-188. YANG Fan, YANG Ping, PENG Daogang. Step response modeling of calibrated furnace temperature and its MCPPID realtime control[J]. Journal of Electric Power Science and Technology,2020,35(6):187-188.

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