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An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions

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  • Muhammad Shahid Wasim
  • Muhammad Amjad
  • Muhammad Abbas Abbasi
  • Abdul Rauf Bhatti
  • Akhtar Rasool

Abstract

Global maximum power point (GMPP) tracking under shading conditions with low tracking time and reduced startup oscillations is one of the challenging tasks in photovoltaic (PV) systems. To cope with this challenge, an improved grasshopper optimization algorithm (IGOA) is proposed in this work to track the GMPP under partial shading conditions (PSC). The performance of the proposed approach is compared with well-known swarm intelligence techniques (SITs) such as gray wolf optimization (GWO), cuckoo search algorithm (CSA), salp swarm algorithm (SSA), improved SSA based on PSO (ISSAPSO), and GOA in terms of tracking time, settling time, failure rate, and startup oscillations. For a fair comparison, the PV system is analysed under uniform irradiance and three PSCs having four to six peaks in the power-voltage characteristic curves and using three to six search agents for each SIT. For this purpose, a PV system containing six solar panels has been built using MATLAB/SIMULINK software, and statistical analysis is performed in detail. The results show that the IGOA tracks the GMPP in 0.07 s and settles the output in 0.12 s which is 25% to 96% faster than its counterparts. Moreover, IGOA proves its consistency with a minimal tracking failure rate of 0% for four to six search agents with negligible startup oscillations. This work is expected to be helpful to PV system installers in obtaining maximum benefits from the installed system.

Suggested Citation

  • Muhammad Shahid Wasim & Muhammad Amjad & Muhammad Abbas Abbasi & Abdul Rauf Bhatti & Akhtar Rasool, 2023. "An improved grasshopper-based MPPT approach to reduce tracking time and startup oscillations in photovoltaic system under partial shading conditions," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-24, August.
    • Handle: RePEc:plo:pone00:0290669
    DOI: 10.1371/journal.pone.0290669
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    References listed on IDEAS

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    1. Jouda Arfaoui & Hegazy Rezk & Mujahed Al-Dhaifallah & Feki Elyes & Mami Abdelkader, 2019. "Numerical Performance Evaluation of Solar Photovoltaic Water Pumping System under Partial Shading Condition using Modern Optimization," Mathematics, MDPI, vol. 7(11), pages 1-18, November.
    2. Izhar Ul Haq & Qudrat Khan & Safeer Ullah & Shahid Ahmed Khan & Rini Akmeliawati & Mehmood Ashraf Khan & Jamshed Iqbal, 2022. "Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-29, January.
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