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All issues Volume 330 (2025) EPJ Web Conf., 330 (2025) 02003 References
Open Access
Issue
EPJ Web Conf.
Volume 330, 2025
The 5th International Conference on Electrical Sciences and Technologies in the Maghreb (CISTEM 2024)
Article Number 02003
Number of page(s) 7
Section Advanced Control for Electric Machines and Drives
DOI https://doi.org/10.1051/epjconf/202533002003
Published online 30 June 2025
  1. M. Wiatros-Motyka, N. Fulghum, D. Jones, Global electricity review 2024, Ember, 2024.M. Wiatros-Motyka, N. Fulghum, D. Jones, Global electricity review 2024, Ember, 2024 [Google Scholar]
  2. M. A. S. Ali, K. K. Mehmood, S. Baloch, and C. H. Kim, Modified rotor-side converter control design for improving the LVRT capability of a DFIG-based WECS. Elec. Pr Sys Res. 186, (2020). [Google Scholar]
  3. M. Bouderbala, B. Bossoufi, A. Lagrioui, M. Taoussi, H. A. Aroussi, and Y. Ihedrane, Direct and indirect vector control of a doubly fed induction generator based in a wind energy conversion system. Inte. J. of Elec and Comp. Eng. 9, no. 3, pp. 1531–1540, (2019). [CrossRef] [Google Scholar]
  4. H. Cui, X. Li, G. Wu, Y. Song, X. Liu, and D. Luo, Mpc based coordinated active and reactive power control strategy of dfig wind farm with distributed esss. Energies (Basel).. 14, no. 13, Jul, (2021). [Google Scholar]
  5. S. S. Nawaz and S. T. Kalyani, Reactive power control and performance analysis of doubly fed induction generatorin micro grid. Indo. J. of Elect. Eng. and Comp. Sc. 28, no. 3, pp. 1214–1226, Dec (2022). [Google Scholar]
  6. H. Benbouhenni, A. Driss, and S. Lemdani, Indirect active and reactive powers control of doubly fed induction generator fed by three-level adaptive-network-based fuzzy inference system – pulse width modulation converter with a robust method based on super twisting algorithms. Elec. Eng. and Electro. no. 4, pp. 31–38, Jul. 2021. https://doi.org/10.20998/2074-272X.2021.4.04 [Google Scholar]
  7. T. Douadi, Y. Harbouche, R. Abdessemed, I. Bakhti Improvement Performances of Active and Reactive Power Control Applied to DFIG for Variable Speed Wind Turbine Using Sliding Mode Control and FOC. Inter. J. of Eng. 31. Oct. (2018). [Google Scholar]
  8. H. Mellah, A. Maafa, H. Sahraoui, A. Yahiou, and H. Smail. Generalized Predictive Control of the Active and Reactive Stator Powers of the DFIG for Wind Energy Generation. Eurasia Proceedings of Science, Technology, Engineering and Mathematics, ISRES Publishing. 295–305, (2023). [Google Scholar]
  9. D. Khan, J. A. Ansari, S. A. Khan, and U. Abrar, “Power optimization control scheme for doubly fed induction generator used in wind turbine generators,” Inventions. 5, no. 3, 1–13. ( 2020). [Google Scholar]
  10. M. Hodzic and L. C. Tai,”Grey Predictor reference model for assisting particle swarm optimization for wind turbine control. Renew Energy. 86. 251–256, ( 2016). [CrossRef] [Google Scholar]
  11. J. Taghinezhad and S. Sheidaei,”Prediction of operating parameters and output power of ducted wind turbine using artificial neural networks. En Rep. 8, 3085–3095, Nov. (2022). [Google Scholar]
  12. W. Liu, C. Li, Y. Liu, and Q. Wu, Predictive control of wind turbine for load reduction during ramping events. Inter. J. of Elec. P. and En.Sys. 93, 135–145, (2017). [Google Scholar]
  13. G. Wu, C. Zhang, C. Cai, K. Yang, and K. Shi, Uncertainty prediction on the angle of attack of wind turbine blades based on the field measurements. Energy. 200, 117515, (2020). [CrossRef] [Google Scholar]
  14. M. Bouderbala, B. Bossoufi, H. A. Aroussi, M. Taoussi, and A. Lagrioui, Novel deadbeat predictive control strategy for DFIG’s back to back power converter. Inter. J. of P. Electro and D. Sys. 13, 139–149, (2022). [Google Scholar]
  15. E. L. K. El Mjabber and A. Khamlichi, A Hybrid Dynamic Nonlinear Controller For Variable Speed Wind Turbine In Low Wind Velocity Regime. Adv.in Elec and Electro. Eng. 22, 115–126, ( 2024). [Google Scholar]
  16. E. K. El Mjabber, A. El Hajjaji, and A. Khamlichi, A hybrid adaptive controller based on sliding mode control and rbf neural network for variable speed wind turbine. Inter. Rev.of App Sc. and Eng,. 7, 61–70, ( 2016). [Google Scholar]
  17. C. Li, Y. Cao, B. Li, B. Liu, F. Qiao, and P. Chen, A novel low voltage ride-through scheme for DFIG based on the cooperation of hybrid energy storage system and crowbar circuit. J. Energy Storage, 73, (2023). [Google Scholar]
  18. O. Zamzoum, Y. El Mourabit, M. Errouha, A. Derouich, and A. El Ghzizal, Active and Reactive Power Control of Wind Turbine based on Doubly Fed Induction Generator using Adaptive Sliding Mode Approach. 10 (2019). DOI: 10.14569/IJACSA.2019.0100252 https://thesai.org/Publications/ViewPaper?Volume=10&Issue=2&Code=IJACSA&SerialNo=52 [Google Scholar]
  19. C. Padilla, R. Hashemi, N. H. Mahmood, and M. Latva-aho, A Nonlinear Autoregressive Neural Network for Interference Prediction and Resource Allocation in URLLC Scenarios. Nov. 2021, [Online]. Available: http://arxiv.org/abs/2111.15630 [Google Scholar]
  20. R. Taherdangkoo, A. Tatomir, M. Taherdangkoo, P. Qiu, an M. Sauter, Nonlinear autoregressive neural networks to predict hydraulic fracturing fluid leakage into shallow groundwater. Water (Switzerland), vol. 12, (2020). [Google Scholar]

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