Intelligent load control strategy based on RBF neural network
Author:
Affiliation:

Clc Number:

TM732

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Aiming at the problem that the traditional PI controller used for the control of electric springs has poor adjustment performance and the control method does not take into account the sudden changes of non-critical loads, an smart load control method is proposed based on RBF neural network of the network on the basis of the mathematical model and control circuit of electric spring.The RBF neural network algorithm is used to make up for the shortcomings of the traditional PI controller that the parameters are fixed and cannot be changed.The real-time online adjustment of the controller parameters reduces the intelligent load instability and ensures the stability of the system bus voltage.Simulation verification in the simulation environment of Matlab/Simulink shows that, compared with traditional PI control, the intelligent load under the proposed control strategy has better performance in regulating the system.Therefore, the smart load under the new PI control strategy based on RBF neural network has better robustness and system control capability.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:March 23,2020
  • Revised:April 19,2020
  • Adopted:August 18,2020
  • Online: September 30,2020
  • Published: September 28,2020