Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (5): 236-241.doi: 10.11896/jsjkx.190400096

• Artificial Intelligence • Previous Articles     Next Articles

Improved Fruit Fly Algorithm for Photovoltaic MPPT Control Strategy

FU Zi-yi1, CHENG Bing1, SHAO Lu-lu2   

  1. 1 School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo,Henan 454000,China
    2 School of Electronic Science and Engineering,University of Electronic Science and Technology of China,Chengdu 610000,China
  • Received:2019-04-16 Online:2020-05-15 Published:2020-05-19
  • About author:GONG R X,ZHONG R R,LIU C,et al.Neutral-Point Potential Balancing Control of Three-Level Discontinuous PWM PV-Inverters.Journal of Chongqing University of Technology(Natural Science),2016,30(9):87-94.
    FU Zi-yi,born in 1958,Ph.D,professor,Ph.D supervisor.His main research interests include intelligent algorithm,motor driving and control,etc.
    CHENG Bing,born in 1990,postgra-duate.His main research interests include intelligent algo and photovoltaic power generation.
  • Supported by:
    This work was supported by the Key Scientific and Technological Project of Henan Province (112102210004).

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Abstract: Local shading will reduce the efficiency of photovoltaic power generation system.Under local shading conditions,the output power characteristic curve of photovoltaic system will produce multiple peaks.The traditional maximum power tracking method does not have the ability of global search,and will fail in multi-peak maximum power tracking.Fruit fly optimization algorithm (FOA) has the ability of global optimization,but in the solution process,there are some problems such as slow convergence speed,low convergence accuracy and easy convergence to local optimum.This paper improves fruit fly algorithm,and proposes a Lévy-FOA algorithm combined with adaptive Lévy flight step size.This algorithm makes full use of the characteristics of Lévy flight non-uniform random walk and introduces adaptive step adjustment factor.It improves the position updating method of the original algorithm,improves the convergence speed and accuracy of the algorithm,and avoids the algorithm falling into local extremum.In this paper,three standard functions are used to analyze the convergence of adaptive Lévy-FOA algorithm,and the results are compared with those of conventional FOA algorithm and adaptive improved learning factor particle swarm optimization (APSO).The comparison results show that compared with FOA algorithm and APSO algorithm,the average tracking time of adaptive Lévy-FOA algorithm is significantly reduced,and the average convergence accuracy is improved by four orders of magnitude.Finally,the adaptive Lévy-FOA algorithm is applied to the maximum power tracking of photovoltaic system.The simulation results show that under different illumination conditions,the adaptive Lévy-FOA algorithm can achieve maximum power tracking with fewer iterations,and can achieve more than 90% of the maximum powerafter the first iteration.Compared with other algorithms,the adaptive Lévy-FOA algorithm has shorter tracking time and higher tracking accuracy,and has superior practical optimization ability,which can improve the output efficiency of photovoltaic system.

Key words: Adaptive, Fruit Fly Algorithm, Lévy Flight, MPPT, Photovoltaic power generation

CLC Number: 

  • TP301.6
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