Neural Networks and Particle Swarm Optimization Based MPPT for Small Wind Power Generator
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.
[1] Hui Li, K. L Shi and P. G. McLaren, "Neuarl-Network-Based Sensorrless
Maximum Wind Energy Capture With Compensated Power Coefficient,"
IEEE Transaction on Industry Applications. Vol. 41. No.6,
November/December 2005.
[2] M. Veerachary, T. Senjyu, and K. Uezato, "Neural Network Based
Maximum Power Point Tracking of Coupled Inductor Interleaved Boost
Converter Supplied PV System using Fuzzy Controller," IEEE
Transactions on Industrial Electronics, Vol. 50, No. 4, pp. 749-758,
August 2003.
[3] Martin T. Hagan, Howard B. Demuth, Mark H. Beale, "Neural network
design," University of Colorado Bookstore, 2002
[4] Clerc, Maurice," Particle Swarm Optimization," Paul & Co. Pub
Consortium, 2006.
[5] J. Kennedy, R. Eberhart, "Particle swarm optimization," in Proc. of IEEE
International Conference on Neural Network, vol. IV, Perth, Australia,
pp. 1942-1948, 1995.
[1] Hui Li, K. L Shi and P. G. McLaren, "Neuarl-Network-Based Sensorrless
Maximum Wind Energy Capture With Compensated Power Coefficient,"
IEEE Transaction on Industry Applications. Vol. 41. No.6,
November/December 2005.
[2] M. Veerachary, T. Senjyu, and K. Uezato, "Neural Network Based
Maximum Power Point Tracking of Coupled Inductor Interleaved Boost
Converter Supplied PV System using Fuzzy Controller," IEEE
Transactions on Industrial Electronics, Vol. 50, No. 4, pp. 749-758,
August 2003.
[3] Martin T. Hagan, Howard B. Demuth, Mark H. Beale, "Neural network
design," University of Colorado Bookstore, 2002
[4] Clerc, Maurice," Particle Swarm Optimization," Paul & Co. Pub
Consortium, 2006.
[5] J. Kennedy, R. Eberhart, "Particle swarm optimization," in Proc. of IEEE
International Conference on Neural Network, vol. IV, Perth, Australia,
pp. 1942-1948, 1995.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56969", author = "Chun-Yao Lee and Yi-Xing Shen and Jung-Cheng Cheng and Yi-Yin Li and Chih-Wen Chang", title = "Neural Networks and Particle Swarm Optimization Based MPPT for Small Wind Power Generator", 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.", keywords = "Maximum power point tracking, artificial neuralnetwork, particle swarm optimization.", volume = "3", number = "12", pages = "2329-7", }