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Neural Computing and Applications
Published in: Neural Computing and Applications 24/2021

22-07-2021 | Original Article

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

Authors: Fuad Alhaj Omar, Ahmet Afsin Kulaksiz

Published in: Neural Computing and Applications | Issue 24/2021

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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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Metadata
Title
Experimental evaluation of a hybrid global maximum power tracking algorithm based on modified firefly and perturbation and observation algorithms
Authors
Fuad Alhaj Omar
Ahmet Afsin Kulaksiz
Publication date
22-07-2021
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 24/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06310-1

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