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YUAN Lin,CHENG Haijun,ZHAO Fengxian.Maximum Power Point Tracking of Multi-peak PV Array Using Adaptive Fruit Fly Optimization Algorithm[J].JOURNAL OF POWER SUPPLY,2020,18(3):168-174
Maximum Power Point Tracking of Multi-peak PV Array Using Adaptive Fruit Fly Optimization Algorithm
Received:June 07, 2018  Revised:September 10, 2019
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DOI:10.13234/j.issn.2095-2805.2020.3.168
Keywords:occlusion  maximum power point tracking(MPPT)  adaptive fruit fly optimization algorithm(AFOA)
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YUAN Lin School of Electrical Engineering, Liaoning University of Technology, Jinzhou , China
CHENG Haijun School of Electrical Engineering, Liaoning University of Technology, Jinzhou , China
ZHAO Fengxian School of Electrical Engineering, Liaoning University of Technology, Jinzhou , China
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Abstract:
      In practical applications, there is often a shaded phenomenon for a photovoltaic(PV) array, and the cor-responding PV output curve has multi-peak characteristics, which is different from those under uniform illumination. Mo-st of the conventional maximum power point tracking(MPPT) methods will stop after they have found the first peak, which will probably cause the PV array to fall into the local extreme point and further fail. An adaptive fruit fly optimization algorithm(AFOA) is proposed to improve the initial value set by the original fruit fly algorithm and the optimal step size as well. In addition, the perturb & observe(P&O) method is combined to improve the algorithm’s real-time capability. Thro-ugh Matlab simulations, the tracking results using AFOA are compared with those using the P&O and particle swarm optimization(PSO) algorithms under uniform illumination and occlusion, respectively. Simulation results show that the AFOA algorithm can accurately track the global maximum power point with or without occlusion, thus improving the system’s stability of output power and the power generation efficiency.
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