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

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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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Authors and Affiliations

  1. Physics Department, Faculty of Sciences, Ferhat Abbas University, Cite El-Bez, 19000, Sétif, Algeria

    Abdelghani Harrag

  2. CCNS Laboratory, Electronics Department, Faculty of Technology, Ferhat Abbas University, Cite Maabouda, 19000, Sétif, Algeria

    Abdelghani Harrag

  3. Electrical Engineering Department, Faculty of Technology, Mohamed Boudiaf University, Route B.B.A., 28000, Msila, Algeria

    Sabir Messalti

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  1. Abdelghani Harrag
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  2. Sabir Messalti
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Correspondence to Abdelghani Harrag.

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Harrag, A., Messalti, S. Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions. Neural Comput & Applic 30, 1145–1170 (2018). https://doi.org/10.1007/s00521-016-2757-y

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