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A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance

Author

Listed:
  • Bustani Hadi Wijaya

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan)

  • Ramadhani Kurniawan Subroto

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan)

  • Kuo Lung Lian

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, No. 43, Sec. 4, Keelung Rd, Taipei 106, Taiwan)

  • Nanang Hariyanto

    (School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40116, Indonesia)

Abstract

The partial shading of photovoltaic (PV) modules due to clouds or blocking objects, such as buildings or tree leaves, is a common problem for photovoltaic systems. To address this, maximum power point tracking (MPPT) is implemented to find the global maximum power point (GMPP). In this paper, a new hybrid MPPT is proposed that combines a modified grasshopper optimization algorithm (GOA) with incremental conductance (IC). In the first stage, the proposed modified GOA is implemented to find a suitable tracking area where the GMPP is located. Then the system moves to the second stage by implementing IC to get the correct GMPP. IC is a fast-performing and reliable algorithm. By combining GOA and IC, the proposed method can find the GMPP accurately with a short tracking time. Various experimental results show that the proposed method yields the highest tracking efficiency and lowest tracking time compared to some of the state-of-the-art MPPT algorithms, such as particle swarm and modified firefly optimizations.

Suggested Citation

  • Bustani Hadi Wijaya & Ramadhani Kurniawan Subroto & Kuo Lung Lian & Nanang Hariyanto, 2020. "A Maximum Power Point Tracking Method Based on a Modified Grasshopper Algorithm Combined with Incremental Conductance," Energies, MDPI, vol. 13(17), pages 1-19, August.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4329-:d:401809
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    References listed on IDEAS

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    1. Seyedmahmoudian, M. & Horan, B. & Soon, T. Kok & Rahmani, R. & Than Oo, A. Muang & Mekhilef, S. & Stojcevski, A., 2016. "State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 435-455.
    2. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
    3. Kannan, Nadarajah & Vakeesan, Divagar, 2016. "Solar energy for future world: - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 1092-1105.
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    Cited by:

    1. Pei Ye, Song & Hua Liu, Yi & Chung Wang, Shun & Yu Pai, Hung, 2022. "A novel global maximum power point tracking algorithm based on Nelder-Mead simplex technique for complex partial shading conditions," Applied Energy, Elsevier, vol. 321(C).
    2. Cătălin Alexandru, 2023. "PV Tracking Systems," Energies, MDPI, vol. 16(6), pages 1-3, March.
    3. Akinyemi Ayodeji Stephen & Kabeya Musasa & Innocent Ewean Davidson, 2022. "Modelling of Solar PV under Varying Condition with an Improved Incremental Conductance and Integral Regulator," Energies, MDPI, vol. 15(7), pages 1-22, March.
    4. Hanaa Mohammed Osman, 2022. "Survey of using grasshopper algorithm," Technium, Technium Science, vol. 4(1), pages 37-44.
    5. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.

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