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Experimental evaluation of a hybrid global maximum power tracking algorithm based on modified firefly and perturbation and observation algorithms

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Abstract

The classical maximum power point tracking (MPPT) approaches are powerful under uniform irradiance conditions. However, under partial shading conditions, they fail to find the global maximum power point (GMPP) and are trapped in one of the local maximum power points (MPPs), resulting in loss of power. This paper presents an experimental investigation of a novel hybrid MPPT approach for photovoltaic systems working under partial shading conditions (PSCs). In the proposed hybrid approach, the firefly algorithm (FA) is modified and employed for global searching through two loops, and the perturbation and observation (P&O) algorithm is used for local searching through one loop. The model of the proposed algorithm is built in the environments of MATLAB/Simulink and Proteus virtual system modeling (VSM) while the experimental study is conducted using a 32-bit microcontroller. The simulation and experimental results are collected under irregular irradiance conditions and PSCs. The results demonstrate that the proposed algorithm exhibits superior performance in the task of finding and tracking the GMPP, shows high sensitivity in capturing any variation in atmospheric conditions, reduces the convergence time to the GMPP and reduces the steady-state oscillation around the optimal operating point. This finding has important implications for developing photovoltaic generation systems.

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Authors and Affiliations

  1. Department of Electric and Energy, Zonguldak Bülent Ecevit University, Zonguldak, Turkey

    Fuad Alhaj Omar

  2. Faculty of Engineering and Natural Sciences, Department of Electrical and Electronics Engineering, Konya Technical University, Selcuklu, Konya, 42250, Turkey

    Ahmet Afsin Kulaksiz

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  1. Fuad Alhaj Omar
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Alhaj Omar, F., Kulaksiz, A.A. Experimental evaluation of a hybrid global maximum power tracking algorithm based on modified firefly and perturbation and observation algorithms. Neural Comput & Applic 33, 17185–17208 (2021). https://doi.org/10.1007/s00521-021-06310-1

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