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A review of estimation of effective wind speed based control of wind turbines

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  • Jena, Debashisha
  • Rajendran, Saravanakumar

Abstract

This paper provides a comprehensive literature review on the estimation of effective wind Speed (EEWS), and EEWS based control techniques applied to wind turbine (WT). Several numbers of good publications have reported the EEWS based control of wind turbine. Wind speed is a driving force for the wind turbine system. In general wind speed measurement is carried out by anemometer which is located at the top of the nacelle. The optimal shaft speed is derived from the exact measurement of wind speed to extract the optimal power output at below rated wind speed. The wind speed provided by the anemometer is measured at a single point of the rotor plane which is not the accurate effective wind speed. At the same time anemometer increases the overall cost, maintenance and reduce the reliability of the overall system. So an accurate EEWS based control technique is required for WT systems to get the optimal power output. In this paper, a detailed description and classification of EEWS and some EEWS based control techniques have been discussed which is based on control strategy and complexity level of WT system. All most all previous work estimates the wind speed using EEWS techniques such as Kalman filter (KF), extended Kalman filter (EKF), neural network (NN) etc., and then different control techniques are applied. In the last section of this paper integral sliding mode control (ISMC) of a WT at below rated speed region is considered. Operating points are determined by proper estimation of effective wind speed, and modified Newton Raphson (MNR) is employed to estimate this. Finally simulation results are presented with a comparison between proposed ISMC, sliding mode control (SMC) and classical controllers such as aerodynamic torque feed forward (ATF) and indirect speed control (ISC).

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  • Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    • Handle: RePEc:eee:rensus:v:43:y:2015:i:c:p:1046-1062
    DOI: 10.1016/j.rser.2014.11.088
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    1. Joselin Herbert, G.M. & Iniyan, S. & Sreevalsan, E. & Rajapandian, S., 2007. "A review of wind energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 11(6), pages 1117-1145, August.
    2. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    3. Seyed Mojtaba Tabatabaeipour & Peter F. Odgaard & Thomas Bak & Jakob Stoustrup, 2012. "Fault Detection of Wind Turbines with Uncertain Parameters: A Set-Membership Approach," Energies, MDPI, vol. 5(7), pages 1-25, July.
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    Cited by:

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