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On Neural Observer in Dynamic Sliding Mode Control of Permanent Magnet Synchronous Wind Generator

Author

Listed:
  • Ali Karami-Mollaee

    (Faculty of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar 9617976487, Iran)

  • Oscar Barambones

    (Automatic Control and System Engineering Department, University of the Basque Country, 01006 Vitoria-Gasteiz, Spain)

Abstract

The captured energy of a wind turbine (WT) can be converted into electricity by a generator. Therefore, to improve the efficiency of this system, both the structures of WTs and generators should be considered for control. But the present challenge is WT uncertainty, while the input signals to the generator should be smooth. In this paper, a permanent magnet synchronous generator (PMSG) is considered. The dynamics of the PMSG can be described using two axes, named d-q reference frameworks, with an input in each framework direction. To obtain the maximum power and to overcome the uncertainty by means of a smooth signal, the dynamic sliding mode controller (D-SMC) is implemented. In the D-SMC, an integrator is placed in the control scheme in order to suppress the chattering, because it acts like a low-pass filter. To estimate the state added by the integrator, a new observer-based neural network (ONN) is proposed. The proof of the stability of the D-SMC and ONN is based on Lyapunov theory. To prove the advantages of the D-SMC, a comparison was also carried out by traditional sliding mode control (T-SMC) with a similar ONN. From this comparison, we know that the advantages of the D-SMC are clear in terms of real implementation, concept, and chattering suppression.

Suggested Citation

  • Ali Karami-Mollaee & Oscar Barambones, 2024. "On Neural Observer in Dynamic Sliding Mode Control of Permanent Magnet Synchronous Wind Generator," Mathematics, MDPI, vol. 12(14), pages 1-18, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2246-:d:1438312
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    References listed on IDEAS

    as
    1. Lunhaojie Liu & Juntao Fei & Xianghua Yang, 2023. "Adaptive Interval Type-2 Fuzzy Neural Network Sliding Mode Control of Nonlinear Systems Using Improved Extended State Observer," Mathematics, MDPI, vol. 11(3), pages 1-20, January.
    2. Bossoufi, Badre & Karim, Mohammed & Lagrioui, Ahmed & Taoussi, Mohammed & Derouich, Aziz, 2015. "Observer backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation," Renewable Energy, Elsevier, vol. 81(C), pages 903-917.
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