Authors

1 Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran,

2 Electrical Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran

Abstract

In this paper presents a Maximum Power Point Tracking (MPPT) technique based on the Hill Climbing Search (HCS) method and fuzzy logic system for Wind Turbines (WTs) including of Permanent Magnet Synchronous Generator (PMSG) as generator. In the conventional HCS method the step size is constant, therefor both steady-state response and dynamic response of method cannot provide at the same time and in the fixed step size of HCS method. The propose method of this paper is improvement the performance HCS method, in order to reach this goal; the fuzzy logic system has been used. The fuzzy logic system based on operation condition determined the step size instantaneously, such as both steady-state response and dynamic response of method be proper at the same time, therefore, efficiency of the new method that used variable step size strategy, will be guaranteed, the results of simulation in environment MATLAB/Simulink software have been shown to be effectiveness of the proposed method.

Keywords

###### ##### References
[1] Kot R, Rolak M. Comparison of maximum peak power
tracking algorithms for a small wind turbine Malinowski.
Math Comput Simul 2013;91:29e40.
[2] Abdullah MA, Yatim AHM, Tan CW, Saidur R. A review of
maximum power point tracking algorithms for wind energy
systems. Renew Sustain Energy Rev2012; 16:3220e7.
[3] Abdullah MA, Yatim AHM, Tan CW. A study of maximum
power point tracking algorithms for wind energy system. In:
IEEE, first conference on clean energy and technology (CET);
2011. pp. 321e6.
[4] Shirazi M, Viki AH, Babayi O. Comparative study of
maximum power extraction strategies in PMSG wind turbine
system. In: IEEE electrical power & energy conference; 2009.
pp. 1e6.
[5] Lei T, Qiang L, Wen-zhuo W. A Gaussian RBF network based
wind speed estimation algorithm for maximum power point
tracking. Energy Proc 2011;12:27e30.
[6] Li H, Shi KL, McLaren P. Neural network based sensor less
maximum wind energy capture with compensated power
coefficient. IEEE Trans Ind Appl 2005;41(6):1548e56.
[7] Galdi V, Piccolo A, Siano P. Exploiting maximum energy
from variable speed wind power generation systems by using
an adaptive Takagie Sugenoe Kang fuzzy model. Energy
Convers Manag 2009;50(2):413e21.
[8] Galdi V, Piccolo A, Siano P. Designing an adaptive fuzzy
controller for maximum wind energy extraction. IEEE Trans
Energy 2008;23(2):559e69.
[9] Ackerman T, editor. Wind power in power systems. John
Wiley & Sons; 2005.
[10]
R. Datta and V. T. Ranganathan, “A method of tracking the peak power points for variable speed wind energy conversion system,” IEEE Trans. Energy Conversion, vol. 18, no. 1, pp. 163–168, Mar. 2003.
[11]
Xing-Peng Li, Wen-Lu Fu, Qing-Jun Shi, Jian-Bing Xu, and Quan-Yuan Jiang “A Fuzzy Logical MPPT Control Strategy for PMSG Wind Generation Systems” journal of electronic science and technology, vol. 11, no. 1, march 2013.