An adaptive neuro-fuzzy inertia controller for variable-speed wind turbines
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DOI: 10.1016/j.renene.2016.01.100
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Cited by:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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Keywords
Doubly fed induction generators; Inertia; Load frequency control; Neuro-fuzzy control; Particle swarm optimization; Variable speed wind turbines;Statistics
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