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A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine

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  • Dali, Ali
  • Abdelmalek, Samir
  • Bakdi, Azzeddine
  • Bettayeb, Maamar

Abstract

This paper proposes a novel Improved Maximum Power Point Tracking (IMPPT) algorithm to extract the maximum available power from a direct-drive Permanent-Magnet Synchronous Generator (PMSG) based standalone Small-scale Variable Speed Wind Turbine (VSWT). The proposed control scheme consists of an IMPPT algorithm to effectively improve the extracted power under various regimes of wind speed. The IMPPT constructs the reference value of the DC voltage for the DC bus. Moreover, a composite low-cost controller (LCC) is also proposed in order to improve the DC voltage tracking based on a new designed nonlinear state observer. The proposed nonlinear robust controller accounts for the overall system dynamics and nonlinear behavior. The objective is to improve the dynamic performance and ensure a good balance of energy conversion efficiency, robustness, cost efficiency, and a simple structure for practical implementation in wind energy conversion systems. Furthermore, the stability of the closed-loop system is analyzed and guaranteed through Lyapunov stability theory. Moreover, two scenarios are used for validation in Matlab/Simulink, including step change and stochastic profiles of wind speed. Simulation results verify the effectiveness and superiority of the IMPPT-LCC approach whereas comparisons with other techniques prove its superiority.

Suggested Citation

  • Dali, Ali & Abdelmalek, Samir & Bakdi, Azzeddine & Bettayeb, Maamar, 2021. "A new robust control scheme: Application for MPP tracking of a PMSG-based variable-speed wind turbine," Renewable Energy, Elsevier, vol. 172(C), pages 1021-1034.
    • Handle: RePEc:eee:renene:v:172:y:2021:i:c:p:1021-1034
    DOI: 10.1016/j.renene.2021.03.083
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    References listed on IDEAS

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    2. Chen, Peng & Han, Dezhi, 2022. "Effective wind speed estimation study of the wind turbine based on deep learning," Energy, Elsevier, vol. 247(C).
    3. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    4. Zholtayev, Darkhan & Rubagotti, Matteo & Do, Ton Duc, 2022. "Adaptive super-twisting sliding mode control for maximum power point tracking of PMSG-based wind energy conversion systems," Renewable Energy, Elsevier, vol. 183(C), pages 877-889.
    5. Farhad Zishan & Lilia Tightiz & Joon Yoo & Nima Shafaghatian, 2023. "Sustainability of the Permanent Magnet Synchronous Generator Wind Turbine Control Strategy in On-Grid Operating Modes," Energies, MDPI, vol. 16(10), pages 1-18, May.

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