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Neuro-fuzzy control for autonomous wind–diesel systems using biomass

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  • Jurado, Francisco
  • Saenz, José R

Abstract

This paper deals with the development of a neuro-fuzzy controller for a wind–diesel system composed of a stall regulated wind turbine with an induction generator connected to an ac bus-bar in parallel with a diesel generator set having a synchronous generator. A gasifier is capable of converting tons of wood chips per day into a gaseous fuel that is fed into a diesel engine. The controller inputs are the engine speed error and its derivative for the governor part of the controller, and the voltage error and its derivative for the automatic voltage regulator. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by tuning the fuzzy logic controllers, optimal time domain performance of the autonomous wind–diesel system can be achieved in a wide range of operating conditions compared to fixed-parameter fuzzy logic controllers and PID controllers.

Suggested Citation

  • Jurado, Francisco & Saenz, José R, 2002. "Neuro-fuzzy control for autonomous wind–diesel systems using biomass," Renewable Energy, Elsevier, vol. 27(1), pages 39-56.
    • Handle: RePEc:eee:renene:v:27:y:2002:i:1:p:39-56
    DOI: 10.1016/S0960-1481(01)00170-7
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    References listed on IDEAS

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    1. Mohamed, Amal Z. & Eskander, Mona N. & Ghali, Fadia A., 2001. "Fuzzy logic control based maximum power tracking of a wind energy system," Renewable Energy, Elsevier, vol. 23(2), pages 235-245.
    2. Papathanassiou, Stavros A & Papadopoulos, Michael P, 2001. "Dynamic characteristics of autonomous wind–diesel systems," Renewable Energy, Elsevier, vol. 23(2), pages 293-311.
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    Cited by:

    1. Jurado, Francisco & Saenz, José R., 2003. "An adaptive control scheme for biomass-based diesel–wind system," Renewable Energy, Elsevier, vol. 28(1), pages 45-57.
    2. Baños, R. & Manzano-Agugliaro, F. & Montoya, F.G. & Gil, C. & Alcayde, A. & Gómez, J., 2011. "Optimization methods applied to renewable and sustainable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1753-1766, May.
    3. 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.
    4. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.

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