基于BP神经网络的刮板输送机变频控制系统优化研究  

Optimization Research of Frequency Conversion System for Scraper Conveyor Using BP Neural Network

作　　者：李丽荣[1] 马继红[1] 常建芳 LI Lirong;MA Jihong;CHANG Jianfang(Handan Polytechnic College,Handan 056001,China)

机构地区：[1]邯郸职业技术学院,河北邯郸056001

出　　处：《煤炭技术》2022年第5期187-189,共3页Coal Technology

基　　金：邯郸市科技局科研项目(1621203036-2)。

摘　　要：针对刮板输送机的负载预测,利用电动机电流为表征量,设计了一种预估电机定子电流的BP神经网络模型,并用于刮板输送机的变频控制优化。以称重信号和电机电流为输入,通过BP神经网络开发预测刮板输送机负载的非线性控制模型。根据预估的负载电流,通过变频控制优化主从驱动电机的工作状态,使刮板输送机的转矩与负载状态相匹配,降低电能损耗。根据主从驱动电机的使能,干预采煤机截割,使其匹配刮板输送机工作状态,提高煤矿生产效率,并降低过载或重载引起的故障率。Aiming at the load forecasting of scraper conveyor,BP neural network for estimating stator current of motor is designed using the motor current as the representative quantity,which is used for the optimization of the frequency conversion control.Taking weighing signal and motor current as input,a nonlinear model for predicting the load is developed using the BP neural network.According to the estimated load current,the working state of the master-slave drive motor is optimized through frequency conversion control,and the torque of the scraper conveyor can matche the load,which will reduce the power loss.According to the enablement of the master-slave drive motor,intervene in the cutting of the shearer to match the working state of the scraper conveyor,which will improve the production efficiency of the coal mine and reduce the failure rate caused by overload or heavy load.

关 键 词：刮板输送机 BP神经网络 变频控制系统 负载预测 

分 类 号：TD63[矿业工程—矿山机电]