Application of BP Neural Network in Power Load Simulator

  • Bing-Da Zhang
  • Ke Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


Adopting the fundamentals of PWM voltage source rectifier and the PID-control technology based on error-back-propagation neural network, an electronic power load simulator is designed in this paper, which can simulate the exact Volt-Ampere characteristics of power load and supply high quality feedback electric power. In order to enable the PID controller to perform better, genetic algorithms are used to accumulate the priori knowledge of the neural network’s connection weights, and the system voltages and the controlled parameters are forecasted. The experimental results show that the electronic power load simulator runs well when the Volt-Ampere characteristics simulated are time-variable or voltage disturbances occur in power system.


Power System Hide Node Connection Weight Genetic Algo Feedback Current 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Liu, J.: Advanced PID Control and its MATLAB Simulation. Publishing House of Electronics Industry, Beijing (2003)Google Scholar
  2. 2.
    Huang, Y.: Self-turning PID Controller Based on Genetic Neural Networks. Journal of System Simulation 15(11), 1628–1630 (2003)Google Scholar
  3. 3.
    Xu, L.: Neural Network Control. Publishing House of Electronics Industry, Beijing (2003)Google Scholar
  4. 4.
    Sakaguchi, A., Yamamoto, T.: A Design of Predictive PID Control Systems Using GA and GMDH Network. In: Lecture Notes in Proceedings of the 2002 IEEE International Conference on Control Applications, Glasgow, Scotland, UK, September 18-20, pp. 266–271 (2002)Google Scholar
  5. 5.
    Rech, C., Grundling, H.A., Hey, H.L., Pinheiro, J.R.: Analysis and Design of a Repetitive Predictive-PID Controller for PWM Inverters, pp. 986–991. IEEE, Los Alamitos (2001)Google Scholar
  6. 6.
    Tan, Y., Dang, X., Achiel, R., Van Cauwenberghe, M.S.: PID Gradient Algorithm for Neural Network Based Generalised Nonlinear PID Controller. Control Theory and Applications 17(6), 861–867 (2000)MATHGoogle Scholar
  7. 7.
    Low, K.S., Chiun, K.Y., Ling, K.V.: Evaluating Generalized Predictive Control for a Brushless DC Drive. IEEE Transactions on Power Electronics 13, 1191–1198 (1998)CrossRefGoogle Scholar
  8. 8.
    Omatu, S., Yoshioka, M.: Self-tuning Neuro-PID Control and Applications. In: IEEE International Conference on Systems, Man, and Cybernetics, pp. 1985–1989 (1997)Google Scholar
  9. 9.
    Martins, D.C., da Silva, P.A., Selinke, R.A.: PID Controller Implementation in Numerical Simulation Program Applied to the Power Static Converter. In: IEEE International Telecommunications Energy Conference, pp. 764–768 (1996)Google Scholar
  10. 10.
    Bohn, C., Atherton, D.P.: An Analysis Package Comparing PID Antiwindup Strategies. IEEE Control Systems Magazine 15(2), 34–40 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bing-Da Zhang
    • 1
  • Ke Zhang
    • 1
  1. 1.School of Electrical Engineering & AutomationTianjin UniversityChina

Personalised recommendations