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Neural network controller for a permanent magnet generator applied in a wind energy conversion system

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  • Eskander, Mona N.

Abstract

In this paper a neural network controller for achieving maximum power tracking as well as output voltage regulation, for a wind energy conversion system (WECS) employing a permanent magnet synchronous generator, is proposed. The permanent magnet generator (PMG) supplies a DC load via a bridge rectifier and two buck–boost converters. Adjusting the switching frequency of the first buck–boost converter achieves maximum power tracking. Adjusting the switching frequency of the second buck–boost converter allows output voltage regulation. The on-times of the switching devices of the two converters are supplied by the developed neural network (NN). The effect of sudden changes in wind speed, and/or in reference voltage on the performance of the NN controller are explored. Simulation results showed the possibility of achieving maximum power tracking and output voltage regulation simultaneously with the developed NN controller. The results proved also the fast response and robustness of the proposed control system.

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  • Eskander, Mona N., 2002. "Neural network controller for a permanent magnet generator applied in a wind energy conversion system," Renewable Energy, Elsevier, vol. 26(3), pages 463-477.
    • Handle: RePEc:eee:renene:v:26:y:2002:i:3:p:463-477
    DOI: 10.1016/S0960-1481(01)00140-9
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    Cited by:

    1. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    2. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    3. Sessarego, Matias & Feng, Ju & Ramos-García, Néstor & Horcas, Sergio González, 2020. "Design optimization of a curved wind turbine blade using neural networks and an aero-elastic vortex method under turbulent inflow," Renewable Energy, Elsevier, vol. 146(C), pages 1524-1535.
    4. La Cava, William & Danai, Kourosh & Spector, Lee & Fleming, Paul & Wright, Alan & Lackner, Matthew, 2016. "Automatic identification of wind turbine models using evolutionary multiobjective optimization," Renewable Energy, Elsevier, vol. 87(P2), pages 892-902.
    5. Konstantinos Mira & Francesca Bugiotti & Tatiana Morosuk, 2023. "Artificial Intelligence and Machine Learning in Energy Conversion and Management," Energies, MDPI, vol. 16(23), pages 1-36, November.

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