粒子群优化BP-PID的矿井提升机调速系统

doi:10.16180/j.cnki.issn1007-7820.2021.01.008

摘要/Abstract

摘要：

传统PID控制器在矿井提升机变频调速系统应用中,由于控制参数固定且不易整定,导致电机转速超调大、电磁转矩和转子磁链脉动大,进而出现矿井提升机调速系统控制效果差的问题。针对这一问题,文中提出一种改进粒子群优化BP神经网络PID控制器的算法。由于BP神经网络算法存在收敛速度慢和极易陷入局部最优的缺点,现将粒子群算法收敛速度快和全局最优特性与神经网络结合,并通过设计神经网络收敛系数进一步加快收敛速度。仿真结果表明,粒子群优化的神经网络控制效果比神经网络好,且效果明显优于传统PID控制器;相较于神经网络PID控制器,矿井提升机转速调节系统稳速调节速度明显提高;与传统PID控制器相比,电机电磁转矩和转子磁链脉动明显降低,具有较强的稳定性和鲁棒性。

关键词: 矿井提升机, 粒子群, 神经网络, 收敛, 脉动, 转子磁链, 电磁转矩

Abstract:

The control parameters of traditional PID controller applied in the mine hoist frequency conversion speed control system is fixed and difficult to be set, which aeaks leads to large speed overshoot and ripples of the electromagnetic torque and rotor flux linkage. In order to improve the performance of the system, an improved particle swarm optimization BP neural network PID controller algorithm of hoist rotor frequency conversion speed regulation system is proposed in the present study. The application of particle swarm algorithm which has fast convergence speed and global optimal characteristics in neural network can overcome the disadvantages of slow convergence speed and easy to fall into local optimum of BP neural network. In addition, the neural network convergence coefficient is designed to further accelerate the convergence speed. The simulation results show that the neural network control effect of particle swarm optimization is better than the neural network, and the effect is obviously better than the traditional PID controller. Compared with the neural network PID controller, the steady speed adjustment speed of the mine hoist speed regulation system of particle swarm optimization is obviously improved. Compared with the traditional PID controller, the ripple of the electromagnetic torque and rotor flux of the motor are significantly reduced, which has strong stability and robustness.

Key words: mine hoist, particle swarm, neural network, convergence, pulsation, rotor flux, electromagnetic torque

中图分类号:

TP183

赵仕艳, 谢子殿, 丁康康, 崔含晴. 粒子群优化BP-PID的矿井提升机调速系统[J]. 电子科技, 2021, 34(1): 43-49.

ZHAO Shiyan, XIE Zidian, DING Kangkang, CUI Hanqing. Particle Swarm Optimization BP-PID of Rotor Variable Frequency Speed in Mine Hoisting System[J]. Electronic Science and Technology, 2021, 34(1): 43-49.

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参考文献 17

[1]	李瑾. 矿井提升机双馈转子变频调速系统设计[J]. 煤矿机械, 2016,37(1):35-37.
	Li Jin. Design of doubly-fed rotor variable frequency speed regulation system of mine hoist[J]. Coal Mine Machinery, 2016,37(1):35-37.
[2]	程国栋, 王华平, 夏晶晶. 高压绕线电动机转子变频调速技术研究[J]. 工矿自动化, 2018,44(8):81-86.
	Cheng Guodong, Wang Huaping, Xia Jingjing. Research on rotor-side frequency conversion speed regulation technology for high voltage wound-rotor motor[J]. Industry and Mine Automation, 2018,44(8):81-86.
[3]	周德华. 矿用绕线式异步电机转子变频矢量控制系统研究[J]. 煤炭技术, 2019,38(1):158-160.
	Zhou Dehua. Study on variable-frequency speed vector control system of wound-rotor induction motor[J]. Coal Technology, 2019,38(1):158-160.
[4]	王晓晨, 李春满. 矿井提升机双馈电机的直接转矩控制[J]. 煤矿机械, 2016,37(2):12-15.
	Wang Xiaochen, Li Chunman. Direct torque control of double fed motor used in mine hoist[J]. Coal Mine Machinery, 2016,37(2):12-15.
[5]	李孟秋, 杨茂骑, 任修勇, 等. 基于BP 神经网络的开关磁阻电机直接转矩控制系统及实现[J]. 电力系统及其自动化学报, 2017,29(1):52-57.
	Li Mengqiu, Yang Maoqi, Ren Xiuyong, et al. Direct torque control system of switched reluctance motor based on BP neural network and its implementation[J]. Proceedings of the CSU-EPSA, 2017,29(1):52-57.
[6]	吉升阳, 汪旭东, 许孝卓, 等. 基于滑模控制的永磁同步电机直接转矩控制研究[J]. 电子科技, 2019,32(7):1-5.
	Ji Shengyang, Wang Xudong, Xu Xiaozhuo, et al. Research on direct torque control of permanent magnet synchronous motor based on sliding mode control[J]. Electronic Science and Technology, 2019,32(7):1-5.
[7]	王亮, 王旭红. 基于小波神经网络的感应电机直接转矩控制[J]. 电力科学与工程, 2015,31(12):9-15.
	Wang Liang, Wang Xuhong. Direct torque control of induction motor based on wavelet neural network[J]. Electric Power Science and Engineering, 2015,31(12):9-15.
[8]	孙亮. 采用改进神经网络PID控制的车辆悬架振动仿真研究[J]. 井冈山大学学报, 2019,40(4):67-71.
	Sun Liang. Research on vehicle suspension vibration simulation using improved neural network PID control[J]. Journal of Jinggangshan University (Natural Science), 2019,40(4):67-71.
[9]	Abderrahim Z, Sabir M, Abdelghani H. A new improved DTC of doubly fed induction machine using GA-based PI controller[J]. Ain Shams Engineering Journal, 2018,9:1877-1885.
[10]	杜美君, 张伟, 谢亚莲. 基于粒子群算法的 PID 控制器参数优化[J]. 电子科技, 2019,32(6):7-11.
	Du Meijun, Zhang Wei, Xie Yalian. Particle swarm optimization based PID controller parameter optimization[J]. Electronic Science and Technology, 2019,32(6):7-11.
[11]	王莉, 张紫烨, 郭晓东, 等. 基于粒子群优化 BP 神经网络的心电信号分类方法[J]. 自动化与仪表, 2019,34(9):84-87.
	Wang Li, Zhang Ziye, Guo Xiaodong, et al. Electrocardiographic signal classification method based on particle swarm optimization BP neural network[J]. Automation and Instrumentation, 2019,34(9):84-87.
[12]	欧青立, 张磊, 邓鹏, 等. 粒子群优化BP神经网络PID控制注塑机液压系统[J]. 应用科技, 2018,45(4):50-55.
	Ou Liqing, Zhang Lei, Deng Peng, et al. Particle swarm optimization BP neural network PID control hydraulic system of injection molding machine[J]. Applied Science and Technology, 2018,45(4):50-55.
[13]	黄璇, 郭立红, 李姜, 等. 改进粒子群优化BP神经网络的目标威胁估计[J]. 吉林大学学报(工学版), 2017,47(3):996-1002.
	Huang Xuan, Guo Lihong, Li Jing, et al. Target threat assessment based on BP neural network optimized by modified particle swarm optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2017,47(3):996-1002.
[14]	王雪松, 许爱德, 赵中林, 等. 粒子群优化递归神经网络的SRM磁链观测器[J]. 电气工程学报, 2017,12(10):1-8.
	Wang Xuesong, Xu Aide, Zhao Zhonglin, et al. Stator flux observer of SRM based on particle swarm optimized recurrent neural network[J]. Journal of Electrical Engineering, 2017,12(10):1-8.
[15]	贺婷婷, 陆军, 丁进良, 等. 原油总碳含量的粒子群优化集成神经网络预测模型[J]. 控制理论与应用, 2019,36(2):192-198.
	He Tingting, Lu Jun, Ding Jinliang, et al. Prediction model of total carbon content of crude oil using ensemble random weights neural network optimized by particle swarm optimization[J]. Control Theory and Applications, 2019,36(2):192-198.
[16]	侯雪峰, 张九根, 梁星. 粒子群优化神经网络PID控制在湿法烟气脱硫[J]. 煤炭技术, 2018,37(4):327-330.
	Hou Xuefeng, Zhang Jiugen, Liang Xing. Particle swarm optimization PID neural network application in wet flue gas desulfurization[J]. Coal Technology, 2018,37(4):327-330.
[17]	王茂林, 叶小红, 王洪光, 等. 液压系统中粒子群优化神经网络权值的控制算法[J]. 应用科技, 2014,41(3):51-54.
	Wang Maolin, Xie Xiaohong, Wang Hongguang, et al. Control algorithm based on particle swarm optimization neural network weights in hydraulic system[J]. Applied Science and Technology, 2014,41(3):51-54.

参数	传统PID	BP-PID	PSO-BP-PID
转速调节时间/s	3.6	4.9	2.5
转速超调/r·min^-1	20	0	0
转矩脉动/N·m	450	250	250
转子磁链脉动/Wb	0.10	0.03	0.03