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Sliding mode extremum seeking control based on improved invasive weed optimization for MPPT in wind energy conversion system

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  • Hu, Lu
  • Xue, Fei
  • Qin, Zijian
  • Shi, Jiying
  • Qiao, Wen
  • Yang, Wenjing
  • Yang, Ting

Abstract

The sliding mode extremum seeking control (SMESC) could track the maximum power point (MPP) of wind energy conversion system (WECS) without wind speed or wind turbine parameters. Inappropriate SMESC parameters would cause steady-state oscillation and increase tracking time. This paper proposed an improved invasive weed optimization (IIWO) to optimize the SMESC parameters. The algorithm developed a new stochastic reproductive strategy to enhance its robustness and simplify the coding. Meanwhile, IIWO optimized double parameters coordinately to replace traditional parameter setting methods of SMESC, which could make the parameters meet the different requirements simultaneously for high efficiency. Simulation results showed that proposed IIWO-SMESC method yielded a better transient response, steady-state stability, and robustness than traditional hill-climbing search (HCS) and SMESC method.

Suggested Citation

  • Hu, Lu & Xue, Fei & Qin, Zijian & Shi, Jiying & Qiao, Wen & Yang, Wenjing & Yang, Ting, 2019. "Sliding mode extremum seeking control based on improved invasive weed optimization for MPPT in wind energy conversion system," Applied Energy, Elsevier, vol. 248(C), pages 567-575.
    • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:567-575
    DOI: 10.1016/j.apenergy.2019.04.073
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    References listed on IDEAS

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    5. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    6. Wang, Jian-jun & Deng, Yu-cong & Sun, Wen-biao & Zheng, Xiao-bin & Cui, Zheng, 2023. "Maximum power point tracking method based on impedance matching for a micro hydropower generator," Applied Energy, Elsevier, vol. 340(C).
    7. 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).
    8. Zouheyr, Dekali & Lotfi, Baghli & Abdelmadjid, Boumediene, 2021. "Improved hardware implementation of a TSR based MPPT algorithm for a low cost connected wind turbine emulator under unbalanced wind speeds," Energy, Elsevier, vol. 232(C).
    9. Xiang Li & Jing Qian & Danning Tian & Yun Zeng & Fei Cao & Lisheng Li & Ganyuan Zhang, 2023. "Maximum Power Tracking Control of Wind Turbines Based on a New Prescribed Performance Function," Energies, MDPI, vol. 16(10), pages 1-21, May.
    10. Erdal Bekiroglu & Muhammed Duran Yazar, 2022. "MPPT Control of Grid Connected DFIG at Variable Wind Speed," Energies, MDPI, vol. 15(9), pages 1-19, April.
    11. Riyadh Rouabhi & Abdelghafour Herizi & Ali Djerioui, 2024. "Performance of Robust Type-2 Fuzzy Sliding Mode Control Compared to Various Conventional Controls of Doubly-Fed Induction Generator for Wind Power Conversion Systems," Energies, MDPI, vol. 17(15), pages 1-25, July.
    12. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    13. Sarkhosh S Chaharborj & Norsarahaida Amin, 2020. "Controlling the pressure of hydrogen-natural gas mixture in an inclined pipeline," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    14. Felix Dietrich & Steffen Borchers-Tigasson & Till Naumann & Horst Schulte, 2021. "Adaptive Extremum Seeking Control of Urban Area Wind Turbines," Energies, MDPI, vol. 14(5), pages 1-12, March.

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