A MPPT strategy based on cuckoo search for wind energy conversion system

  • Authors

    • C Centhil Kumar ProfessorDepartment of Mechanical SREYAS Institute of Engineering and Technology
    • I Jacob Raglend
    2018-09-17
    https://doi.org/10.14419/ijet.v7i4.17366
  • Wind Turbine, Doubly Fed Induction Generator, Fuzzy Logic Controller, MPPT, Cuckoo Search Algorithm.

  • The WECS based Doubly Fed Induction Generator (DFIG) system is presented in this paper which includes different MPPT control strategies for a grid connected system. The GSC gives the flow of power from the rotor part of DFIG up to the grid and the modulation of DC voltage. Here the cuckoo search algorithm based on MPPT is designed, to obtain a higher power from the changing speed wind turbine. The algorithms such as Perturb and Observe (P&O), Proportional Integral (PI) control and Fuzzy Logic Controller (FLC) are compared and their performances are evaluated. To design and develop the cuckoo search optimization based on MPPT for WECS, and to obtain optimum voltage regulation and power, thus improving the working performance, reducing the domain time and minimizing the performance indices. To simulate the different MPPT control methods, MATLAB/Simulink environment is used here.

     

  • References

    1. [1] Vijay Chand Ganti, Bhim Singh, Shiv Kumar Aggarwal, and Tara Chandra Kandpal (2012). “DFIG-Based Wind Power Conversion with Grid Power Leveling for Reduced Gustsâ€, IEEE Transactions on Sustainable Energy, Vol. 3(1), pp.12-20.https://doi.org/10.1109/TSTE.2011.2170862.

      [2] M. Matteo, M. Riccardo, D. Vincenzo, and A. Riccardo (2017). “A new model for environmental and economic evaluation of renewable energy systems: The case of wind turbinesâ€, Applied Energy, Vol. 189, pp.739-752.https://doi.org/10.1016/j.apenergy.2016.11.124.

      [3] B Yang, XS Zhang, T Yu, HC Shu, and ZH Fang (2017). “Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbineâ€, Energy Conversion and Management, Vol.133, pp. 427–443.https://doi.org/10.1016/j.enconman.2016.10.062.

      [4] N. Delgarm, B. Sajadi, F. Kowsary, and Delgarm S (2016). “Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO)â€, Applied Energy, Vol. 170, pp.293–303.https://doi.org/10.1016/j.apenergy.2016.02.141.

      [5] M. Wieczorek, and M. Lewandowski (2017). “A mathematical representation of an energy management strategy for hybrid energy storage system in electric vehicle and real time optimization using a genetic algorithmâ€, Applied Energy, Vol. 192, pp.222–233.https://doi.org/10.1016/j.apenergy.2017.02.022.

      [6] L. Wei (1998). “Design of a hybrid fuzzy logic proportional plus conventional integral derivative controllerâ€. IEEE Transactions on Fuzzy Systems, Vol. 6(4), pp.449–463.https://doi.org/10.1109/91.728430.

      [7] T.M. Jabban, M.A. Alali, A.Z. Mansoor, and A.N. Hamoodi (2012). “Enhancing the step response curve for rectifier current of HVDC system based on artificial neural network controllerâ€, Journal of King Saud University-Engineering Science, Vol. 24(2), pp.181-192.https://doi.org/10.1016/j.jksues.2012.01.002.

      [8] X Zhang, Q Li, M Yin, X Ye, and Y Zou (2012). “An improved hill-climbing searching method based on halt mechanismâ€, ZhongguoDianjiGongchengXuebao, Proceedings of the CSEE, Vol. 32 (14), pp.128–134.

      [9] A.O. Deepa, R. Lal Raja Singh and R. Leena Rose (2016). “A novel energy management system using renewable distribution generation unitsâ€, Journal of Electrical Engineering and Science, Vol. 2(2), pp.1-11.https://doi.org/10.18831/djeee.org/2016021001.

      [10] ZM Dalala, ZU Zahid, W Yu, Y Cho, and JS Lai (2013). “Design and analysis of an MPPT technique for small-scale wind energy conversion systemsâ€, IEEE Transaction on Energy Conversion, Vol. 28(3), pp.756-767.https://doi.org/10.1109/TEC.2013.2259627.

      [11] SMR Kazmi, H Goto, HJ Guo, and O Ichinokura (2011). “A novel algorithm for fast and efficient speed-sensorless maximum power point tracking in wind energy conversion systemsâ€, IEEE Transaction on Industrial Electronics, Vol. 58(1), pp.29–36.https://doi.org/10.1109/TIE.2010.2044732.

      [12] Z Zhang Z, Y Zhao, W Qiao, and LY Qu (2014), “A space-vector modulated sensorless direct-torque control for direct-drive PMSG wind turbinesâ€. IEEE Transactions on Industry Applications, Vol. 50(4), pp. 2331–2341.https://doi.org/10.1109/TIA.2013.2296618.

      [13] HM Nguyen and D.S. Naidu (2012). “Direct fuzzy adaptive control for standalone wind energy conversion systemsâ€, In: Proceedings of the world congress on engineering and computer science, Vol. 2, pp. 994-999.

      [14] MA Mayosky, and GIE Cancelo (1999). “Direct adaptive control of wind energy conversion systems using Gaussian networksâ€, IEEE Transaction on Neural Network, Vol. 10(4).https://doi.org/10.1109/72.774245.

      [15] SangitaRoy, and SheliSinhaChaudhuri (2013). “Cuckoo Search Algorithm using Levy Flight: A Review", International Journal of Modern Education and Computer Science, pp: 10-15.https://doi.org/10.5815/ijmecs.2013.12.02.

      [16] M. Mary Linda, and M. AnjanaRency (2015). “DC-DC converter control unit for variable speed wind turbineâ€, Journal of Electrical Engineering and Science, Vol. 1(1), pp. 43-52.https://doi.org/10.18831/djeee.org/2015011005.

  • Downloads

  • How to Cite

    Centhil Kumar, C., & Jacob Raglend, I. (2018). A MPPT strategy based on cuckoo search for wind energy conversion system. International Journal of Engineering & Technology, 7(4), 2298-2303. https://doi.org/10.14419/ijet.v7i4.17366