Log in

Current Control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

  • Research
  • Published:
Journal of Fusion Energy Aims and scope Submit manuscript

Abstract

The primary performance index of the fast control power supply in the Experimental Advanced Superconducting Tokamak (EAST) is to quickly track the reference current signal, realize the excitation of the load coil with the output current, and feedback control the vertical displacement of the plasma. The current on the load coil of EAST fast control power supply is affected by various uncertain environmental factors, making it difficult to establish a standard mathematical model for prediction. Accurate object model is not required in grey prediction, and only a small amount of known information is needed to achieve short-term prediction of output current. Grey prediction has been studied and applied in EAST fast control power supply to some extent. To further improve prediction accuracy and accelerate output current response speed, an improved grey prediction algorithm is proposed to achieve output current prediction. Considering the control delay in digital control, the output current of the next period is predicted using the sampled original sequence. Following the principle of new information priority, an original sequence transformation operator is proposed to weight new information. The predicted output current in the next period is added to the original sequence while removing the oldest original sequence, to achieve rolling prediction of the output current in the next two periods. The control value of the output current is loaded one switching period in advance, further improving prediction accuracy while compensating for control delay. The output gain of proportional integral (PI) control is adaptively adjusted based on the error between the predicted current and the reference current, and the improved grey prediction variable gain PI control achieves fast and accurate control of the output current. Simulation and experimental results show that the proposed control method has high prediction accuracy. Compared to traditional PI control and grey prediction control, the proposed control method can effectively improve the output current response speed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data Availability

This declaration is “not applicable”.

References

  1. H. Huang, N. Bi, H. Wang, Exploration of the voltage control mode of second-generation EAST fast control power supply. IEEE Plasma Sci. 46(5), 1684–1688 (2018). https://doi.org/10.1109/TPS.2017.2773620

    Article  ADS  Google Scholar 

  2. T. Wang, P. Fu, Y. Hu et al., A novel real-time feedback compensation system associated with inductive voltage generated by plasma in the EAST PF coil quench detection system. Fus. Eng. Des. 145, 11–17 (2019). https://doi.org/10.1016/j.fusengdes.2019.05.003

    Article  Google Scholar 

  3. L. MacKinnon, H. Li, C.L.E. Swartz, Robust model predictive control with embedded multi-scenario closed-loop prediction. Comput. Chem. Eng. 149, 107283–107298 (2021). https://doi.org/10.1016/j.compchemeng.2021.107283

    Article  Google Scholar 

  4. A. Sodiq, H. Iqbal, Robust deadbeat finite-set predictive current control with torque oscillation and noise reduction for PMSM drives. IEEE Trans. Ind. Appl. 58(1), 365–374 (2022). https://doi.org/10.1109/TIA.2021.3130022

    Article  Google Scholar 

  5. D. Zhou, A. Al-Durra, K. Zhang et al., A robust prognostic indicator for renewable energy technologies: A novel error correction grey prediction model. IEEE Trans. Ind. Electron. 66(12), 11–17 (2019). https://doi.org/10.1109/TIE.2019.2893867

    Article  Google Scholar 

  6. J.-S. Lee, Y.-C. Lee, An application of grey prediction to transmission power control in mobile sensor networks. IEEE Internet Things J. 5(3), 190–203 (2021). https://doi.org/10.1109/JIO.2018.2826008

    Article  Google Scholar 

  7. O.H. Keangseok, S.E.O. Jaho, Development of an adaptive and weighted model predictive control algorithm for autonomous driving with disturbance estimation and grey prediction. IEEE Access 10, 35251–35264 (2022). https://doi.org/10.1109/ACCESS.2022.3163309

    Article  Google Scholar 

  8. K. Xua, X. Luo, X. Pang, A new multivariable grey model and its application to energy consumption in China. J. Intell. Fuzzy Syst. 42, 3153–3168 (2022). https://doi.org/10.3233/JIFS-210822

    Article  Google Scholar 

  9. X. Huang, P. Guan, H. Pan et al., Research on grey predictive control of PMSM based on reduced-order Luenberger observer. J. Electr. Eng. Technol. 16, 2635–2646 (2021). https://doi.org/10.1007/s42835-021-00797-3

    Article  Google Scholar 

  10. Y. Wei, Y. Wei, Y. Sun et al., A Smith structure-based delay compensation method for model predictive current control of PMSM system. IEEE Trans. Emerg. Sel. Top. Power Electron. 10(4), 4090–4101 (2022). https://doi.org/10.1109/JESTPE.2021.3137299

    Article  MathSciNet  Google Scholar 

  11. W.-K. Sou, P.-I. Chan, C. Gong et al., Finite-set model predictive control for hybrid active power filter. IEEE Trans. Ind. Electron. 70(1), 52–64 (2023). https://doi.org/10.1109/TIE.2022.3146550

    Article  Google Scholar 

  12. Q. **ao, Y. **, H. Jia et al., Modulated model predictive control for multilevel cascaded H-bridge converter-based static synchronous compensator. IEEE Trans. Ind. Electron. 69(2), 1091–1102 (2022). https://doi.org/10.1109/TIE.2021.3056953

    Article  Google Scholar 

  13. Z. Cui, J. Wu, Z. Ding et al., A hybrid rolling grey framework for short time series modelling. Neural Comput. Appl. 33, 11339–11353 (2021). https://doi.org/10.1007/s00521-020-05658-0

    Article  Google Scholar 

  14. X. Shan, Y. Cao, Forecasting Guangdong’s marine science and technology, marine economy, and employed persons by coastal regions-based on rolling grey MGM(1, m) model. Water 14(5), 824–840 (2022). https://doi.org/10.3390/w14050824

    Article  Google Scholar 

  15. C.-L. Ho, Y.-S. Lin, A study on disabling injuries prediction of Taiwan occupational disaster with grey rolling model. IEEE Trans. Math. Probl. Eng. 1, 1306602–1306617 (2022). https://doi.org/10.1155/2022/1306602

    Article  Google Scholar 

  16. Q. Sun, S. Wang, S. Gao et al., A state of charge estimation approach for lithium–ion batteries based on the optimized metabolic EGM(1,1) algorithm. Batteries 8(12), 260–279 (2022). https://doi.org/10.3390/batteries8120260

    Article  Google Scholar 

  17. Y. Wang, J. Lu, Improvement and application of GM(1,1) model based on multivariable dynamic optimization. J. Syst. Eng. Electron. 31(3), 593–601 (2020). https://doi.org/10.23919/JSEE.2020.000024

    Article  Google Scholar 

  18. C. Gao, Z. Hu, Z. **ong et al., Grey prediction evolution algorithm based on accelerated even grey model. IEEE Access 8, 107941–107957 (2020). https://doi.org/10.1109/ACCESS.2020.3001194

    Article  Google Scholar 

  19. R. Huang, X. Fu, Y. Pu, A novel fractional accumulative grey model with GA-PSO optimizer and its application. Sensors 23, 636–650 (2023). https://doi.org/10.3390/s23020636

    Article  ADS  Google Scholar 

  20. H. Zhu, Multi-parameter grey prediction model based on the derivation method. Appl. Math. Model. 97, 588–601 (2021). https://doi.org/10.1016/j.apm.2021.04.016

    Article  MathSciNet  Google Scholar 

  21. R. Yao, S. **, C. Wei et al., A novel robust grey model for forecasting Chinese electricity demand. Discrete Dyn. Nat. Soc. 1, 2182748–2182759 (2022). https://doi.org/10.1155/2022/2182748

    Article  Google Scholar 

  22. K. Li, P. **ong, Y. Wu et al., Forecasting greenhouse gas emissions with the new information priority generalized accumulative grey model. Sci. Total Environ. 807, 150859–150872 (2022). https://doi.org/10.1016/j.scitotenv.2021.150859

    Article  ADS  Google Scholar 

  23. Q. Shen, Q. Shi, T. Tang et al., A novel weighted fractional GM(1,1) model and its applications. Complexity 1, 6570683–6570704 (2020). https://doi.org/10.1155/2020/6570683

    Article  Google Scholar 

  24. H. Wang, Z. Zhang, A novel grey model with conformable fractional opposite-direction accumulation and its application. Appl. Math. Model. 108, 585–611 (2022). https://doi.org/10.1016/j.apm.2022.04.020

    Article  MathSciNet  Google Scholar 

  25. P. Jiang, Y. **ng, No-load cutting-in control of the doubly fed induction generator based on grey prediction PI control. Energy Rep. 7, 38–48 (2021). https://doi.org/10.1016/j.egyr.2021.10.055

    Article  Google Scholar 

  26. L. Hou, X. Li, M. Wang, Grey-fuzzy PI optimal control of MMC-HVDC system, in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China, pp. 3258–3262 (2020). https://doi.org/10.1109/EI250167.2020.9347281

Download references

Funding

This work was supported by Regional Innovation and Development Joint Fund of National Natural Science Foundation of China (No. U22A20225).

Author information

Authors and Affiliations

Authors

Contributions

ZC and HH wrote the main manuscript text and HW prepared other material. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Zhao Chen or Haihong Huang.

Ethics declarations

Competing interests

This declaration is “not applicable”.

Ethical Approval

This declaration is “not applicable”.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, Z., Huang, H. & Wang, H. Current Control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI. J Fusion Energ 42, 34 (2023). https://doi.org/10.1007/s10894-023-00370-y

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10894-023-00370-y

Keywords

Navigation