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Journal of Applied Sciences

Year: 2013 | Volume: 13 | Issue: 22 | Page No.: 5377-5383
DOI: 10.3923/jas.2013.5377.5383
Improved Immune Clonal Selection Algorithm for Photovoltaic Maximum Power Point Tracking Control
Ruo-Fa Cheng, Si-Zhong Zhang, Guan-Qing Guo and Deng-Feng Peng

Abstract: Maximum power point (MPPT) real-time tracking for photovoltaic system is a kind of typical problem. This study is forward an Improved Clonal Selection Algorithm (ICSA) through introducing cloning operator to solve real-time MPPT. The algorithm which introducing reasonable clonal selection rate, clonal proliferation rate, mutation rate can effectively improve the convergence speed and avoid prematurity. The simulation model is established by using the equivalent circuit of photovoltaic cell in MATLAB/SIMULINK. The ICSA is implemented using m file under the MATLAB environment. Experimental results show that the proposed algorithm has a remarkable quality of the global convergence reliability and convergence velocity. This algorithm can effectively track the maximum power point in real-time in case of variable temperature and light intensity.

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How to cite this article
Ruo-Fa Cheng, Si-Zhong Zhang, Guan-Qing Guo and Deng-Feng Peng, 2013. Improved Immune Clonal Selection Algorithm for Photovoltaic Maximum Power Point Tracking Control. Journal of Applied Sciences, 13: 5377-5383.

Keywords: Photovoltaic cells, maximum power point tracking, clonal selection algorithm and simulation

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