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A novel-strategy controller design for maximum power extraction in stand-alone windmill systems

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  • Ganjefar, Soheil
  • Ghasemi, Ali Akbar

Abstract

This paper proposes a new control strategy for an optimal extraction of output power from stand-alone windmill systems. The system consists of a variable-speed wind turbine directly coupled to a PMSG (permanent magnet synchronous generator), a diode bridge rectifier, a DC-to-DC boost converter, and a battery bank. This control method, with its higher speed, directly creates a control signal for handling DC-to-DC converters. Adding an ESN (echo state network) to this method can result in extracting maximum power from the wind turbine without measuring the wind speed. This system is then simulated using MATLAB-SIMULINK software. The obtained simulation results show that the objective of extracting maximum power from the wind is reached.

Suggested Citation

  • Ganjefar, Soheil & Ghasemi, Ali Akbar, 2014. "A novel-strategy controller design for maximum power extraction in stand-alone windmill systems," Energy, Elsevier, vol. 76(C), pages 326-335.
    • Handle: RePEc:eee:energy:v:76:y:2014:i:c:p:326-335
    DOI: 10.1016/j.energy.2014.08.024
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    References listed on IDEAS

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    1. 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.
    2. Kesraoui, M. & Korichi, N. & Belkadi, A., 2011. "Maximum power point tracker of wind energy conversion system," Renewable Energy, Elsevier, vol. 36(10), pages 2655-2662.
    3. Ganjefar, Soheil & Ghassemi, Ali Akbar & Ahmadi, Mohamad Mehdi, 2014. "Improving efficiency of two-type maximum power point tracking methods of tip-speed ratio and optimum torque in wind turbine system using a quantum neural network," Energy, Elsevier, vol. 67(C), pages 444-453.
    4. González, L.G. & Figueres, E. & Garcerá, G. & Carranza, O., 2010. "Maximum-power-point tracking with reduced mechanical stress applied to wind-energy-conversion-systems," Applied Energy, Elsevier, vol. 87(7), pages 2304-2312, July.
    5. Abdullah, M.A. & Yatim, A.H.M. & Tan, C.W. & Saidur, R., 2012. "A review of maximum power point tracking algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3220-3227.
    6. Lin, Whei-Min & Hong, Chih-Ming, 2010. "Intelligent approach to maximum power point tracking control strategy for variable-speed wind turbine generation system," Energy, Elsevier, vol. 35(6), pages 2440-2447.
    7. Kot, R. & Rolak, M. & Malinowski, M., 2013. "Comparison of maximum peak power tracking algorithms for a small wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 29-40.
    8. Hong, Chih-Ming & Ou, Ting-Chia & Lu, Kai-Hung, 2013. "Development of intelligent MPPT (maximum power point tracking) control for a grid-connected hybrid power generation system," Energy, Elsevier, vol. 50(C), pages 270-279.
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    Cited by:

    1. Shin Young Heo & Mun Kyeom Kim & Jin Woo Choi, 2015. "Hybrid Intelligent Control Method to Improve the Frequency Support Capability of Wind Energy Conversion Systems," Energies, MDPI, vol. 8(10), pages 1-22, October.
    2. Ganjefar, Soheil & Mohammadi, Ali, 2016. "Variable speed wind turbines with maximum power extraction using singular perturbation theory," Energy, Elsevier, vol. 106(C), pages 510-519.
    3. Gu, Ya-jing & Yin, Xiu-xing & Liu, Hong-wei & Li, Wei & Lin, Yong-gang, 2015. "Fuzzy terminal sliding mode control for extracting maximum marine current energy," Energy, Elsevier, vol. 90(P1), pages 258-265.
    4. Phan, Dinh-Chung & Yamamoto, Shigeru, 2016. "Rotor speed control of doubly fed induction generator wind turbines using adaptive maximum power point tracking," Energy, Elsevier, vol. 111(C), pages 377-388.

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