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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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    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).
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    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. 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).
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    13. 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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