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Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions

Authors
  • Harrag, Abdelghani1, 2
  • Messalti, Sabir3
  • 1 Ferhat Abbas University, Physics Department, Faculty of Sciences, Cite El-Bez, Sétif, 19000, Algeria , Sétif (Algeria)
  • 2 Ferhat Abbas University, CCNS Laboratory, Electronics Department, Faculty of Technology, Cite Maabouda, Sétif, 19000, Algeria , Sétif (Algeria)
  • 3 Mohamed Boudiaf University, Electrical Engineering Department, Faculty of Technology, Route B.B.A., Msila, 28000, Algeria , Msila (Algeria)
Type
Published Article
Journal
Neural Computing and Applications
Publisher
Springer London
Publication Date
Dec 08, 2016
Volume
30
Issue
4
Pages
1145–1170
Identifiers
DOI: 10.1007/s00521-016-2757-y
Source
Springer Nature
Keywords
License
Yellow

Abstract

This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels in series and associated parallel, as well as an algorithm that controls the improvement of the overall performance under different shading conditions. The obtained results using MATLAB/Simulink simulation show improvement rating varying between 106.49 and 171.03%, which is huge compared to a static configuration operating below the total available power. Another important point is the number of iterations needed to find the optimal configuration (between 6 and 132 for a population of 50 configurations tested at each generation); this means that in the worst case (132 iterations), the proposed algorithm performed 132 × 50 = 6600 configurations instead of 1616 = 1.84 × 1019 necessary in case of exhaustive search to test all possible configurations. This last point is very important in the implementation of the proposed system in auto-tuning of the system in real-time condition. Besides using genetic algorithm to track the optimal configuration, our main contribution consists of improving the output power while reducing the number of switching by keeping PV modules, if possible, in same position (0 switching) or on the same line/column (1 switching) in few iteration needing only two sensors one for the voltage and another for the current of the PV array.

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