煤矿带式输送机开关磁阻电动机半直驱系统DITC控制策略

刘鹏, 张磊, 鲍久圣, 陈华新, 魏春基, 马传明, 王雷, 王小龙

刘鹏,张磊,鲍久圣,等. 煤矿带式输送机开关磁阻电动机半直驱系统DITC控制策略[J]. 工矿自动化,2024,50(12):93-102, 127. DOI: 10.13272/j.issn.1671-251x.2024060040
引用本文: 刘鹏,张磊,鲍久圣,等. 煤矿带式输送机开关磁阻电动机半直驱系统DITC控制策略[J]. 工矿自动化,2024,50(12):93-102, 127. DOI: 10.13272/j.issn.1671-251x.2024060040
LIU Peng, ZHANG Lei, BAO Jiusheng, et al. DITC control strategy for semi-direct drive system with switched reluctance motor in coal mine belt conveyor[J]. Journal of Mine Automation,2024,50(12):93-102, 127. DOI: 10.13272/j.issn.1671-251x.2024060040
Citation: LIU Peng, ZHANG Lei, BAO Jiusheng, et al. DITC control strategy for semi-direct drive system with switched reluctance motor in coal mine belt conveyor[J]. Journal of Mine Automation,2024,50(12):93-102, 127. DOI: 10.13272/j.issn.1671-251x.2024060040

煤矿带式输送机开关磁阻电动机半直驱系统DITC控制策略

基金项目: 平顶山天安煤业股份有限公司重点科研项目资助(2023120045);江苏高校优势学科建设工程资助项目(PAPD)。
详细信息
    作者简介:

    刘鹏(1989—),男,河南平顶山人,工程师,主要从事煤矿机电运输系统大型设备管理及煤矿智能化技术应用工作,E-mail:444769779@qq.com

    通讯作者:

    鲍久圣(1979—),男,安徽桐城人,教授,博士,主要从事矿山运输及其智能化技术研究工作,E-mail:cumtbjs@cumt.edu.cn

  • 中图分类号: TD67

DITC control strategy for semi-direct drive system with switched reluctance motor in coal mine belt conveyor

  • 摘要:

    平顶山天安煤业九矿有限责任公司井下带式输送机采用异步电动机+液力偶合器+减速装置的驱动系统,存在传动效率低、传动链长、调速性能差等问题。对该驱动系统进行改造,设计了一种带式输送机2×400 kW开关磁阻电动机半直驱(SRSD)系统。采用BP神经网络预测开关磁阻电动机(SRM)磁链和转矩,基于预测值建立了高精确SRM非线性模型。结合SRM在换相区的转矩变化规律与PWM控制思想,提出了一种改进型直接瞬时转矩控制(DITC)策略,以转矩误差作为输入量,在转矩误差阈值内对相电流进行PWM控制。针对带式输送机空载和变负载运行工况进行仿真,结果表明改进型DITC策略较传统DITC策略可显著降低SRM转矩脉动,最大降幅达39.1%,提高了带式输送机SRSD系统运行稳定性。基于SRM关键结构参数与改进型DITC策略试制了带式输送机SRSD系统,并在煤矿井下进行了工业性试验,结果表明SRSD系统可实现带式输送机满载平稳启动,动态特性良好,且耗电量较原驱动系统减少了24%。

    Abstract:

    The underground belt conveyor at Pingdingshan Tian'an Coal Mining No. 9 Co., Ltd. uses a drive system consisting of an asynchronous motor, hydraulic coupling, and reduction gears. This system exhibits issues such as low transmission efficiency, lengthy transmission chain, and poor speed regulation performance. To address these limitations, the drive system was retrofitted with a 2×400 kW switched reluctance motor semi-direct drive(SRSD) system utilizing a switched reluctance motor(SRM) for the belt conveyor. A BP neural network was used to predict the flux linkage and torque of the SRM, and a highly accurate SRM nonlinear model was developed based on the predictions. By combining the torque variation patterns of the SRM in the commutation zone and the PWM control principles, an improved direct instantaneous torque control(DITC) strategy was proposed. Torque error was used as the input, and PWM control was applied to phase currents within the torque error threshold in this strategy. Simulations under no-load and variable load conditions of the belt conveyor were conducted. The results showed that the improved DITC strategy significantly reduced the torque ripple of SRM compared to the traditional DITC strategy, with a maximum reduction of 39.1%, thereby improving the operational stability of the SRSD system for the belt conveyor. Based on the key structural parameters of the SRM and the improved DITC strategy, the SRSD system for belt conveyors was developed and tested in an underground coal mine. The results showed that the SRSD system enabled smooth full-load startup, demonstrated excellent dynamic performance, and reduced power consumption by 24% compared to the original drive system.

  • 图  1   SRSD系统结构[13]

    Figure  1.   Structure of switched reluctance motor semi-direct drive(SRSD) system[13]

    图  2   带式输送机SRSD系统安装

    Figure  2.   SRSD system layout in belt conveyor

    图  3   三相SRM系统模型

    Figure  3.   Model of three-phase switch reluctance motor(SRM) system

    图  4   SRM初始位置

    Figure  4.   SRM initial position

    图  5   SRM网格划分

    Figure  5.   SRM grid division

    图  6   SRM磁感应强度云图

    Figure  6.   SRM magnetic induction intensity cloud map

    图  7   SRM二维输出特性曲线

    Figure  7.   SRM two-dimensional output characteristic curves

    图  8   BP神经网络预测值与实际值对比

    Figure  8.   Comparison between prediction values of BP neural network and the actual ones

    图  9   SRM三维输出特性曲面

    Figure  9.   Three dimensional output characteristic surface of SRM

    图  10   SRM非线性模型

    Figure  10.   Nonlinear SRM model

    图  11   SRM DITC策略原理

    Figure  11.   Direct instantaneous torque control(DITC) strategy principle for SRM

    图  12   换向区不同导通角时SRM转矩

    Figure  12.   SRM torque under different conduction angles in commutation zone

    图  13   不同导通角减小幅度下B,C相转矩

    Figure  13.   B-phase and C-phase torques under different reduction amplitudes of conduction angles

    图  14   不同关断角减小幅度下A相转矩和转矩脉动

    Figure  14.   A-phase torque and torque ripple under different reduction amplitudes of turn off angle

    图  15   SRM改进型DITC仿真模型

    Figure  15.   Simulation model of improved DITC for SRM

    图  16   带式输送机空载运行工况下改进型DITC策略仿真结果

    Figure  16.   Simulation results of improved DITC strategy for belt conveyor under no-load operation conditions

    图  17   带式输送机变负载运行工况下改进型DITC策略仿真结果

    Figure  17.   Simulation results of improved DITC strategy for belt conveyor under variable load operation conditions

    图  18   SRSD系统实物

    Figure  18.   SRSD system

    图  19   带式输送机满载启动工况下SRSD系统转速曲线

    Figure  19.   Speed curve of SRSD system under full load starting condition of belt conveyor

    表  1   改造前带式输送机主要参数

    Table  1   Main parameters of belt conveyor before renovation

    参数 参数
    运输距离/m 500 运量/(t·h−1 500
    带宽/m 1 电动机功率/kW 400
    带速/(m·s−1 4 驱动滚筒直径/mm 1 250
    下载: 导出CSV

    表  2   SRSD系统关键参数

    Table  2   Key parameters of SRSD system

    参数 参数
    装机功率/kW 400 太阳轮齿数 21
    额定电压/V 1140 行星轮齿数 42
    额定转速/(r·min−1 60 内齿圈齿数 105
    减速机构传动比 5
    下载: 导出CSV

    表  3   SRM设计参数

    Table  3   Design parameters of SRM

    参数 参数
    电压/V 1 140 定子内外径/mm 581,943
    转速/(r·min−1 300 转子内外径/mm 200,580
    槽极数 12/8 定子轭厚/mm 50
    气隙/mm 1 转子轭厚/mm 60
    电动机长度/mm 1 056
    下载: 导出CSV

    表  4   磁链预测定量分析

    Table  4   Quantitative analysis of magnetic flux prediction

    序号 角度/(°) 电流/A 仿真值/Wb 预测值/Wb 绝对误
    差/Wb
    相对误
    差/%
    1 9.8 551 5.68 5.780 0.100 1.76
    2 16.7 763 12.23 12.340 0.110 0.90
    3 20.3 975 13.76 13.770 0.010 0.07
    4 11.5 339 5.496 5.591 0.095 1.73
    下载: 导出CSV

    表  5   转矩预测定量分析

    Table  5   Quantitative analysis of torque prediction

    序号 角度/
    (°)
    电流/
    A
    仿真值/
    (N·m)
    预测值/
    (N·m)
    绝对误
    差/(N·m)
    相对误
    差/%
    1 9.8 551 25319.45 25356.15 36.70 0.140
    2 16.7 763 27041.73 27047.69 5.96 0.020
    3 20.3 975 11171.84 11171.40 0.44 0.004
    4 11.5 339 19978.39 19969.10 9.29 0.047
    下载: 导出CSV
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  • 收稿日期:  2024-06-10
  • 修回日期:  2024-12-17
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