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

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    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.


  • Keywords

    Wind Turbine; Doubly Fed Induction Generator; Fuzzy Logic Controller; MPPT; Cuckoo Search Algorithm.

  • References

      [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 Gusts”, 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 turbines”, 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 turbine”, 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)”, 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 algorithm”, 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 controller”. 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 controller”, 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 mechanism”, 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 units”, 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 systems”, 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 systems”, 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 turbines”. 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 systems”, 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 networks”, 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 turbine”, Journal of Electrical Engineering and Science, Vol. 1(1), pp. 43-52.https://doi.org/10.18831/djeee.org/2015011005.




Article ID: 17366
DOI: 10.14419/ijet.v7i4.17366

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.