DU Gang, MA Xiaoping, ZHANG Ping. Research on speed control algorithm of coal mine local ventilator[J]. Journal of Mine Automation, 2020, 46(9): 69-73. DOI: 10.13272/j.issn.1671-251x.2020030025
Citation: DU Gang, MA Xiaoping, ZHANG Ping. Research on speed control algorithm of coal mine local ventilator[J]. Journal of Mine Automation, 2020, 46(9): 69-73. DOI: 10.13272/j.issn.1671-251x.2020030025

Research on speed control algorithm of coal mine local ventilator

More Information
  • At present, coal mine local ventilator system mainly adopts conventional PID control algorithm to carry out frequency-conversion speed-regulation,but the conventional PID control parameter adjustment mainly relies on artificial experience, adjustment time is long, real time is poor, and easy to occur over-regulating and oscillating output of the control quantity. To solve the above problems, a particle swarm optimization (PSO) optimized PID control algorithm was proposed and applied to the speed control of coal mine local ventilator. PSO algorithm is added to the speed control system of coal mine local ventilator based on the conventional PID control algorithm to realize PID control parameter optimization. The conventional PID control part directly runs in accordance with the optimal parameter setting obtained by Z-N tuning method; PSO optimized PID control part randomly generated a set of three-dimensional particles through the algorithm program of S function, and calls the function assignin to assign three-dimensional particles values to Kp,Ki,Kd parameters of speed control system simulation model, taking control system error indicator ITAE as fitness function for iterative optimization, unity of PSO optimization and PID parameter setting optimization is realized.The simulation results show that compared with the conventional PID control, after PSO algorithm optimization, the output performance of local ventilator speed control are improved significantly, especially the overshoot and the regulation time index, and the overshoot is only 20% of the conventional PID control algorithm.
  • Related Articles

    [1]WU Yulun, XIAO Tannan, CHEN Ying. Fault diagnosis method for substations based on fault enumeration tree to generate fuzzy Petri net[J]. Journal of Mine Automation, 2025, 51(1): 85-94. DOI: 10.13272/j.issn.1671-251x.18233
    [2]CHENG Lei, LI Zhengjian, SHI Haorong, WANG Xin. A bottom air temperature prediction model based on PSO-Elman neural network[J]. Journal of Mine Automation, 2024, 50(1): 131-137. DOI: 10.13272/j.issn.1671-251x.2023090062
    [3]FAN Zhanwen, LIU Bo. Research on cooperative control of fully mechanized mining equipment based on improved Elman neural network[J]. Journal of Mine Automation, 2021, 47(S2): 26-28.
    [4]ZHANG Mei, XU Tao, SUN Huihuang, MENG Xiangyu. Fault diagnosis of mine hoist based on fuzzy fault tree and Bayesian network[J]. Journal of Mine Automation, 2020, 46(11): 1-5. DOI: 10.13272/j.issn.1671-251x.17562
    [5]MENG Xiangang, YU Xiao, LI Xiaojing. Fault diagnosis of mine hoist deceleration system based on fuzzy Petri net[J]. Journal of Mine Automation, 2019, 45(6): 91-95. DOI: 10.13272/j.issn.1671-251x.2018120059
    [6]SUN Huiying, LIN Zhongpeng, HUANG Can, CHEN Peng. Fault diagnosis of mine ventilator based on improved BP neural network[J]. Journal of Mine Automation, 2017, 43(4): 37-41. DOI: 10.13272/j.issn.1671-251x.2017.04.009
    [7]SUN Qidong, ZHANG Kairu, SONG Xiangmin, LI Liming, MA Hui, WANG Yi. Research of single-phase fault line selection of power distribution network based on fifth harmonics energy and LM-Elman neural network[J]. Journal of Mine Automation, 2016, 42(8): 61-64. DOI: 10.13272/j.issn.1671-251x.2016.08.015
    [8]LIU Jingyan, LI Yudong, GUO Shunjing. Gear box fault diagnosis based on Elman neural networ[J]. Journal of Mine Automation, 2016, 42(8): 47-51. DOI: 10.13272/j.issn.1671-251x.2016.08.012
    [9]GONG Maofa, LIU Yanni, WANG Laihe, ZHANG Chao, HOU Linyua. Fault diagnosis of mine hoist based on optimizing fuzzy Petri networks[J]. Journal of Mine Automation, 2016, 42(7): 50-53. DOI: 10.13272/j.issn.1671-251x.2016.07.012
    [10]GAO Zhengzhong, GONG Qunying, ZHAO Lina, XU Huanqi, XIAO Jiayi. Fault diagnosis of underground water pump based on fuzzy Petri net and condition monitoring[J]. Journal of Mine Automation, 2016, 42(5): 28-31. DOI: 10.13272/j.issn.1671-251x.2016.05.007

Catalog

    Article Metrics

    Article views (61) PDF downloads (18) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return