Optimization Method Based MPPT for Wind Power Generators
This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078400Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1752
 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.
 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.
 Martin T. Hagan, Howard B. Demuth, Mark H. Beale, "Neural network design," University of Colorado Bookstore, 2002
 Clerc, Maurice," Particle Swarm Optimization," Paul & Co. Pub Consortium, 2006.
 J. Kennedy, R. Eberhart, "Particle swarm optimization," in Proc. of IEEE International Conference on Neural Network, vol. IV, Perth, Australia, pp. 1942-1948, 1995.