Open Science Research Excellence
@article{(International Science Index):http://waset.org/publications/7889,
  title    = {Neural Networks and Particle Swarm Optimization Based MPPT for Small Wind Power Generator},
  author    = {Chun-Yao Lee and  Yi-Xing Shen and  Jung-Cheng Cheng and  Yi-Yin Li and  Chih-Wen Chang},
  country   = {},
  institution={},
  abstract  = {This paper proposes the method combining artificial
neural network (ANN) with particle swarm optimization (PSO) to
implement the maximum power point tracking (MPPT) by controlling
the rotor speed of the wind generator. First, the measurements of wind
speed, rotor speed of wind power generator and output power of wind
power generator are applied to train artificial neural network and to
estimate the wind speed. Second, the method mentioned above is
applied to estimate and control the optimal rotor speed of the wind
turbine so as to output the maximum power. Finally, the result reveals
that the control system discussed in this paper extracts the maximum
output power of wind generator within the short duration even in the
conditions of wind speed and load impedance variation.},
  {International Journal of Electrical and Computer Engineering },  volume    = {3},
  number    = {12},
  year      = {2009},
  pages     = {2222 - 2228},
  ee        = {http://waset.org/publications/7889},
  url       = {http://waset.org/Publications?p=36},
  bibsource = {http://waset.org/Publications},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 36, 2009},
}