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Wind power smoothing using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode

Author

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  • Kamel, Rashad M.
  • Chaouachi, Aymen
  • Nagasaka, Ken

Abstract

The need of reducing CO2 emissions in electricity generation field for solving global warming problems has led to increase interest in Micro-Grid (MG) especially the one with renewable sources such as solar and wind generations. Wind speed fluctuations cause high fluctuations in output power of wind turbine which cause fluctuations in fr and voltages of the MG in the islanding mode and originate stability problems. In this study, a new fuzzy logic pitch controller and an energy storage ultra capacitor are proposed and developed to smooth the output power of wind turbine and enhance MG's performance in islanding mode. These two proposed controllers are compared with the conventional PI pitch controller, which is usually used to control wind generation system when the wind speed exceeds a rated value. Obtained results proved that our two proposed strategies are effective for the MG performance improvement during islanding mode. All models and controllers are developed using Matlab® Simulink® environment.

Suggested Citation

  • Kamel, Rashad M. & Chaouachi, Aymen & Nagasaka, Ken, 2010. "Wind power smoothing using fuzzy logic pitch controller and energy capacitor system for improvement Micro-Grid performance in islanding mode," Energy, Elsevier, vol. 35(5), pages 2119-2129.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:5:p:2119-2129
    DOI: 10.1016/j.energy.2010.01.030
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    2. Lamsal, Dipesh & Sreeram, Victor & Mishra, Yateendra & Kumar, Deepak, 2019. "Output power smoothing control approaches for wind and photovoltaic generation systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
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    5. Mohamed Thameem Ansari, M. & Velusami, S., 2010. "DMLHFLC (Dual mode linguistic hedge fuzzy logic controller) for an isolated wind–diesel hybrid power system with BES (battery energy storage) unit," Energy, Elsevier, vol. 35(9), pages 3827-3837.
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    10. Yin, Xiu-xing & Lin, Yong-gang & Li, Wei & Gu, Ya-jing & Liu, Hong-wei & Lei, Peng-fei, 2015. "A novel fuzzy integral sliding mode current control strategy for maximizing wind power extraction and eliminating voltage harmonics," Energy, Elsevier, vol. 85(C), pages 677-686.
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    13. Pan, Pengcheng & Sun, Yuwei & Yuan, Chengqing & Yan, Xinping & Tang, Xujing, 2021. "Research progress on ship power systems integrated with new energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    14. Raju, S.Krishnama & Pillai, G.N., 2016. "Design and real time implementation of type-2 fuzzy vector control for DFIG based wind generators," Renewable Energy, Elsevier, vol. 88(C), pages 40-50.
    15. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    16. Ou, Ting-Chia & Hong, Chih-Ming, 2014. "Dynamic operation and control of microgrid hybrid power systems," Energy, Elsevier, vol. 66(C), pages 314-323.
    17. de Prada Gil, Mikel & Gomis-Bellmunt, Oriol & Sumper, Andreas & Bergas-Jané, Joan, 2011. "Analysis of a multi turbine offshore wind farm connected to a single large power converter operated with variable frequency," Energy, Elsevier, vol. 36(5), pages 3272-3281.
    18. Jung-Hun Lee & Seung-Gyu Jeon & Dong-Kyu Kim & Joon-Seok Oh & Jae-Eon Kim, 2020. "Temporary Fault Ride-Through Method in Power Distribution Systems with Distributed Generations Based on PCS," Energies, MDPI, vol. 13(5), pages 1-15, March.
    19. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2016. "Comprehensive overview of grid interfaced wind energy generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 260-281.
    20. Obara, Shin’ya & Watanabe, Seizi & Rengarajan, Balaji, 2011. "Operation method study based on the energy balance of an independent microgrid using solar-powered water electrolyzer and an electric heat pump," Energy, Elsevier, vol. 36(8), pages 5200-5213.
    21. Pichan, Mohammad & Rastegar, Hasan & Monfared, Mohammad, 2013. "Two fuzzy-based direct power control strategies for doubly-fed induction generators in wind energy conversion systems," Energy, Elsevier, vol. 51(C), pages 154-162.
    22. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    23. Howlader, Abdul Motin & Urasaki, Naomitsu & Yona, Atsushi & Senjyu, Tomonobu & Saber, Ahmed Yousuf, 2013. "A review of output power smoothing methods for wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 135-146.
    24. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.
    25. Shivarama Krishna, K. & Sathish Kumar, K., 2015. "A review on hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 907-916.

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