基于WNN-PSO的提升机感应电动机矢量控制系统  

Vector control system of induction motor for hoist based on WNN-PSO

作　　者：徐翔 XU Xiang(Luoyang Mining Machinery Engineering Design Institute Co.,Ltd.,Luoyang 471039,Henan,China;State Key Laboratory of Intelligent Mining Heavy Equipment,Luoyang 471039,Henan,China;Luoyang Zhengfangyuan Heavy Mining Machinery Inspection Technology Co.,Ltd.,Luoyang 471039,Henan,China)

机构地区：[1]洛阳矿山机械工程设计研究院有限责任公司,河南洛阳471039 [2]智能矿山重型装备全国重点实验室,河南洛阳471039 [3]洛阳正方圆重矿机械检验技术有限责任公司,河南洛阳471039

出　　处：《矿山机械》2024年第4期20-23,共4页Mining & Processing Equipment

摘　　要：矿井提升机由于提人、提物、频繁启停等对罐笼的定位精度要求较高,因而对感应电动机转速的控制精度要求较高。在感应电动机无速度传感器矢量控制过程中,转速辨识的准确与否直接决定了感应电动机的控制精度。将小波神经网络算法与粒子群优化算法结合后用于转速辨识,采用矢量控制技术,在Simulink上进行了仿真试验,在电动机运行中采集了转速、电流、磁链等物理量,最后试验结果验证了该方法的可行性。Due to the high requirements for the positioning accuracy of the cage,such as lifting people and objects,and frequent starting and stopping,mine hoists required high precision in controlling the speed of the induction motor.In the process of no-speed sensor vector control of the induction motor,the accuracy of the speed identification directly determined the control accuracy of the induction motor.The wavelet neural network algorithm and the particle swarm optimization algorithm were combined for the speed identification.Then,the simulation experiment was carried out on Simulink by using the vector control technology.The physical quantities such as speed,current and magnetic linkage were collected in the operation of the motor.Finally,the experimental results verified the feasibility of the method.

关 键 词：提升机 感应电动机 小波神经网络 粒子群优化 转速辨识 

分 类 号：TD534[矿业工程—矿山机电]