Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (1): 43-49.doi: 10.16180/j.cnki.issn1007-7820.2021.01.008

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Particle Swarm Optimization BP-PID of Rotor Variable Frequency Speed in Mine Hoisting System

ZHAO Shiyan,XIE Zidian,DING Kangkang,CUI Hanqing   

  1. School of Electrical and Control Engineering,Heilongjiang University of Science and Technology,Harbin 150022,China
  • Received:2019-10-30 Online:2021-01-15 Published:2021-01-22
  • Supported by:
    Graduate Innovation Research Fund of Heilongjiang University of Science and Technology(YJSCX2019-106HKD)

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

CLC Number: 

  • TP183