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An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines

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  • Hafiz, Faizal
  • Abdennour, Adel

Abstract

A Variable-Speed Wind Turbine (VSWT) can serve as a good reservoir of Kinetic Energy (KE) for few seconds owing to wide operating rotor speed. Based on this fact, several approaches have been proposed to introduce synthetic inertial response in VSWT. Usually, this is accomplished by introducing an additional control loop at the outer most level of the control hierarchy. However, several key issues including the selection of control parameters and the effects of wind speed variations on the synthetic inertial support are not addressed. As a result, the KE reserve is severely under utilized. To address these concerns, in this work, a simple approach is proposed to control parameter selection which ensures the optimal use of available KE reserve. Further, to tackle the variable KE reserve, a comprehensive inertia controller using intelligent learning paradigm is designed. The proposed inertia controller can adapt to wind speed variations while providing optimum inertial response. Efficacy of the proposed approach is evaluated over the entire operating range of the VSWT. For further evaluation, wind speed data from NREL western wind integration is utilized. The results indicate that the proposed system is quite effective and can maintain an adequate performance over the entire operating range.

Suggested Citation

  • Hafiz, Faizal & Abdennour, Adel, 2016. "An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines," Renewable Energy, Elsevier, vol. 92(C), pages 136-146.
    • Handle: RePEc:eee:renene:v:92:y:2016:i:c:p:136-146
    DOI: 10.1016/j.renene.2016.01.100
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    Cited by:

    1. Nabipour, M. & Razaz, M. & Seifossadat, S.GH & Mortazavi, S.S., 2017. "A new MPPT scheme based on a novel fuzzy approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1147-1169.
    2. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose-Ignacio Sarasua, 2020. "Hybrid Wind–PV Frequency Control Strategy under Variable Weather Conditions in Isolated Power Systems," Sustainability, MDPI, Open Access Journal, vol. 12(18), pages 1-25, September.
    3. Sidra Mumtaz & Laiq Khan & Saghir Ahmed & Rabiah Bader, 2017. "Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-32, September.
    4. Ting-Hsuan Chien & Yu-Chuan Huang & Yuan-Yih Hsu, 2020. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm," Energies, MDPI, Open Access Journal, vol. 13(20), pages 1-15, October.
    5. Clemens Jauch & Arne Gloe, 2019. "Simultaneous Inertia Contribution and Optimal Grid Utilization with Wind Turbines," Energies, MDPI, Open Access Journal, vol. 12(15), pages 1-21, August.
    6. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).

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