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Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System

Author

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  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61517, Egypt)

  • Ahmed Fathy

    (Electrical Engineering Department, Faculty of Engineering, Jouf University, Sakaka 72314, Saudi Arabia
    Electrical Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt)

Abstract

A significant growth in PV (photovoltaic) system installations have been observed during the last decade. The PV array has a nonlinear output characteristic because of weather intermittency. Partial shading is an environmental phenomenon that causes multiple peaks in the power curve and has a negative effect on the efficiency of the conventional maximum power point tracking (MPPT) methods. This tends to have a substantial effect on the overall performance of the PV system. Therefore, to enhance the performance of the PV system under shading conditions, the global MPPT technique is mandatory to force the PV system to operate close to the global maximum. In this paper, for the first time, a stochastic fractal search (SFS) optimization algorithm is applied to solve the dilemma of tracking the global power of PV system based triple-junction solar cells under shading conditions. SFS has been nominated because it can converge to the best solution at a fast rate. Moreover, balance between exploration and exploitation phases is one of its main advantages. Therefore, the SFS algorithm has been selected to extract the global maximum power point (MPP) under partial shading conditions. To prove the superiority of the proposed global MPPT–SFS based tracker, several shading scenarios have been considered. The idea of changing the shading scenario is to change the position of the global MPP. The obtained results are compared with common optimizers: Antlion Optimizer (ALO), Cuckoo Search (CS), Flower Pollination Algorithm (FPA), Firefly-Algorithm (FA), Invasive-Weed-Optimization (IWO), JAYA and Gravitational Search Algorithm (GSA). The results of comparison confirmed the effectiveness and robustness of the proposed global MPPT–SFS based tracker over ALO, CS, FPA, FA, IWO, JAYA, and GSA.

Suggested Citation

  • Hegazy Rezk & Ahmed Fathy, 2020. "Stochastic Fractal Search Optimization Algorithm Based Global MPPT for Triple-Junction Photovoltaic Solar System," Energies, MDPI, vol. 13(18), pages 1-28, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4971-:d:417339
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    References listed on IDEAS

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    1. Sai Krishna, G. & Moger, Tukaram, 2019. "Improved SuDoKu reconfiguration technique for total-cross-tied PV array to enhance maximum power under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 333-348.
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    10. Ramadan J. Mustafa & Mohamed R. Gomaa & Mujahed Al-Dhaifallah & Hegazy Rezk, 2020. "Environmental Impacts on the Performance of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 12(2), pages 1-17, January.
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