Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

Electrical Engineering Design Method Based on Neural Network and Application of Automatic Control System

  • Zhe, Zhang (College of Information Engineering, Shijiazhuang Vocational College of Finance and Economics) ;
  • Yongchang, Zhang (College of Information Engineering, Shijiazhuang Vocational College of Finance and Economics)
  • Received : 2022.04.22
  • Accepted : 2022.09.09
  • Published : 2022.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The existing electrical engineering design method and the dynamic objective function in the application process of automatic control system fail to meet the unbounded condition, which affects the control tracking accuracy. In order to improve the tracking control accuracy, this paper studies the electrical engineering design method based on neural network and the application of automatic control system. This paper analyzes the structure and working mechanism of electrical engineering automation control system by an automation control model with main control objectives. Following the analysis, an optimal solution of controllability design and fault-tolerant control is figured out. The automatic control power coefficient is distributed based on an ideal control effect of system. According to the distribution results, an automatic control algorithm is based on neural network for accurate control. The experimental results show that the electrical automation control method based on neural network can significantly reduce the control following error to 3.62%, improve the accuracy of the electrical automation tracking control, thus meeting the actual production needs of electrical engineering automation control system.

Keywords

References

  1. D. L. Mon-Nzongo, P. G. Ipoum-Ngome, R. C. C. Flesch, J. Song-Manguelle, T. Jin, and J. Tang, "Synchronous-frame decoupling current regulators for induction motor control in high-power drive systems: Modelling and design," IET Power Electronics, vol. 13, no. 4, pp. 669-679, 2020. https://doi.org/10.1049/iet-pel.2019.0222
  2. F. Heydarpour, E. Abbasi, M. J. Ebadi, and S. M. Karbassi, "Solving an optimal control problem of cancer treatment by artificial neural networks," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 6, no. 4, pp. 18-25, 2020. https://doi.org/10.9781/ijimai.2020.11.011
  3. W. L. Li, "Research on intelligent electric automation control simulation," Microcomputer Applications, vol. 37, no. 1, pp. 127-130,
  4. Q. W. Ding, C. Li, W. B. Yuan, W. X. Hao, and W. Yu, "Effects of heave plate on dynamic response of floating wind turbine spar platform under the coupling effects of wind and wave," Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, vol. 39, no. 4, pp. 1113-1126, 2019.
  5. H. Fu, T. Jiang, Y. Cui, and B. Li, "Design and operational strategy research for temperature control systems of isothermal compressed air energy storage power plants," Journal of Thermal Science, vol. 28, pp. 204- 217, 2019. https://doi.org/10.1007/s11630-019-1089-5
  6. F. R. Ismagilov and V. E. Vavilov, "Optimization of a high-temperature starter-generator of inverted design for aircraft," Russian Electrical Engineering, vol. 90, no. 5, pp. 391-396, 2019. https://doi.org/10.3103/s1068371219050067
  7. S. Liang, C. Chen, and G. Zou, "Intelligent driving system of robot based on computer vision and neural network algorithm," Journal of Intelligent and Fuzzy Systems, vol. 38, no. 6, pp. 7279-7290, 2020. https://doi.org/10.3233/JIFS-179803
  8. Z. A. Obaid, M. T. Muhssin, and L. M. Cipcigan, "A model reference-based adaptive PSS4B stabilizer for the multi-machines power system," Electrical Engineering, vol. 102, no. 1, pp. 349-358, 2020. https://doi.org/10.1007/s00202-019-00879-6
  9. S. Barg and K. Bertilsson, "Multi-objective Pareto and GAs nonlinear optimization approach for flyback transformer," Electrical Engineering, vol. 101, no. 3, pp. 995-1006, 2019. https://doi.org/10.1007/s00202-019-00836-3
  10. C. R. Bharathi, "Design of new asymmetrical cascaded multilevel inverter with reduced number of switches," European Journal of Electrical Engineering, vol. 21, no. 6, pp. 547-552, 2019. https://doi.org/10.18280/ejee.210609
  11. M. Gao, "Application analysis of PLC technology in automatic control of ship auxiliary machinery," Journal of Coastal Research, no. 115, no. SI, pp. 130-132, 2020. https://doi.org/10.2112/JCR-SI115-039.1
  12. N. K. Vu and H. Q. Nguyen, "Model order reduction algorithm based on preserving dominant poles," International Journal of Control, Automation and Systems, vol. 19, no. 6, pp. 2047-2058, 2021. https://doi.org/10.1007/s12555-019-0990-8
  13. G. Soorya Priya, and P. Sivakumar, "Analysis of antlion optimizer-based ABT for automatic generation control of an interconnected power system," Soft Computing, vol. 23, no. 18, pp. 8563-8577, 2019. https://doi.org/10.1007/s00500-019-04029-9
  14. S. Owyed, "New exact traveling wave solutions of space-time fractional nonlinear electrical transmission lines equation: arising in electrical engineering," Journal of Intelligent and Fuzzy Systems, vol. 38, no. 3, pp. 2717-2723, 2020. https://doi.org/10.3233/jifs-179557
  15. H. Du, J. Zhai, M. Z. Chen, and W. Zhu, "Robustness analysis of a continuous higher order finite-time control system under sampled-data control," IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2488-2494, 2019. https://doi.org/10.1109/tac.2018.2867603
  16. M. Tryputen, V. Kuznetsov, V. Kuznetsov, Y. Kuznetsova, M. Tryputen, and A. Kuznetsova, "Laboratory bench to analyze of automatic control system with a fuzzy controller," Diagnostyka, vol. 21, no. 2, pp. 61- 68, 2020.
  17. L. T. Xiao, M. Y. Li, W. G. Wang, K. W. Hou, and Y. L. Li, "Implementation of FPGA-based controller in automatic control system platform for launch site," Procedia Computer Science, vol. 166, pp. 26-30, 2020. https://doi.org/10.1016/j.procs.2020.02.007
  18. X. Meng, "Model for reliability evaluation of electrical engineering automation with 2-tuple linguistic information," Journal of Intelligent and Fuzzy Systems, vol. 37, no. 2, pp. 1827-1834, 2019. https://doi.org/10.3233/JIFS-179245
  19. A. Kebir, L. Woodward, and O. Akhrif, "Real-time optimization of renewable energy sources power using neural network-based anticipative extremum-seeking control," Renewable Energy, vol. 134, pp. 914-926, 2019. https://doi.org/10.1016/j.renene.2018.11.083
  20. N. Zerari and M. Chemachema, "Event-triggered adaptive output-feedback neural-networks control for saturated strict-feedback nonlinear systems in the presence of external disturbance," Nonlinear Dynamics, vol. 104, no. 2, pp. 1343-1362, 2021.  https://doi.org/10.1007/s11071-021-06351-0