Commenced in January 2007
Paper Count: 31824
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1334614Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2036
 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.