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New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system

Author

Listed:
  • Medjber, Ahmed
  • Guessoum, Abderrezak
  • Belmili, Hocine
  • Mellit, Adel

Abstract

This work presents a new control strategy to ensure maximum power point tracking for a DFIG (doubly fed induction generator) based WECS (wind energy conversion system). The proposed strategy uses neural networks and fuzzy logic controllers to control the power transfer between the machine and the grid using the indirect vector control and reactive power control techniques. This transfer is ensured by controlling the rotor via two identical converters. The first converter is connected to the RSC (rotor side) and the second is connected to the GSC (grid side) via a filter. The DC (Direct Current) link voltage is controlled by a fuzzy controller. This control strategy is used to control the rotor side currents and to protect the generator by limiting the output current (or voltage).

Suggested Citation

  • Medjber, Ahmed & Guessoum, Abderrezak & Belmili, Hocine & Mellit, Adel, 2016. "New neural network and fuzzy logic controllers to monitor maximum power for wind energy conversion system," Energy, Elsevier, vol. 106(C), pages 137-146.
    • Handle: RePEc:eee:energy:v:106:y:2016:i:c:p:137-146
    DOI: 10.1016/j.energy.2016.03.026
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    References listed on IDEAS

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    1. 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.
    2. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    3. Lin, Whei-Min & Hong, Chih-Ming & Cheng, Fu-Sheng, 2010. "Fuzzy neural network output maximization control for sensorless wind energy conversion system," Energy, Elsevier, vol. 35(2), pages 592-601.
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    Citations

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    Cited by:

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    2. Das, Soubhagya K. & Verma, Deepak & Nema, Savita & Nema, R.K., 2017. "Shading mitigation techniques: State-of-the-art in photovoltaic applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 369-390.
    3. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    4. Fathy, Ahmed & Rezk, Hegazy & Yousri, Dalia & Kandil, Tarek & Abo-Khalil, Ahmed G., 2022. "Real-time bald eagle search approach for tracking the maximum generated power of wind energy conversion system," Energy, Elsevier, vol. 249(C).
    5. Bodha, Venugopal Reddy & Srujana, A. & Chandrashekar, O., 2018. "A modified H-bridge voltage source converter with Fault Ride Capability," Energy, Elsevier, vol. 165(PB), pages 1380-1391.

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