Maximum power point tracking (MPPT) system of small wind power generator using RBFNN approach

Authors:
Chun-Yao Lee;Po-Hung Chen;Yi-Xing Shen
Affiliations:
Department of Electrical Engineering, Chung Yuan Christian University, Chung Li City, Taiwan;Department of Electrical Engineering, St. John's University, Taipei County, Taiwan;Department of Electrical Engineering, Chung Yuan Christian University, Chung Li City, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

A novel approach of combination of radial basis function neural network (RBFNN) and particle swarm optimization (PSO) is proposed to achieve the maximum power point tracking (MPPT) in this study. The measured data of the small wind generator (250W), including wind speed, generator speed and output power of wind power generator, are applied to estimate the wind speed and output power by the proposed wind speed ANN"w"i"n"d and power estimation ANN"P"e-PSO modules, respectively. Using the predicted results by the two modules of Matlab/Simulink, the MPPT point can be obtained by manipulating the generator speeds. The experimental results show that the proposed RBFNN-based approach can increase the maximum output power of the wind power generator even if the wind speed and load varies.