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A novel global maximum power point tracking algorithm based on Nelder-Mead simplex technique for complex partial shading conditions

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  • Pei Ye, Song
  • Hua Liu, Yi
  • Chung Wang, Shun
  • Yu Pai, Hung

Abstract

In this study, a novel global maximum power point tracking (GMPPT) algorithm for photovoltaic generation system (PGS) operating under complex partial shading conditions is proposed. The presented GMPPT technique is based on Nelder-Mead (NM) simplex technique, which is commonly used to solve complicated optimization problems and has advantages such as simple implementation, derivative-free nature, fast convergence, and high accuracy. In this study, simulation results of 252 shading patterns with non-repeatable irradiance levels and 243 shading patterns under repeatable irradiance levels are provided to validate the effectiveness of the proposed GMPPT methods. According to the simulated and experimental results, the presented NM-based GMPPT approach has the highest success rate and tracking accuracy under all the tested shading patterns compared with other GMPPT methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:321:y:2022:i:c:s030626192200722x
    DOI: 10.1016/j.apenergy.2022.119380
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    References listed on IDEAS

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    1. Jian Zhao & Xuesong Zhou & Youjie Ma & Yiqi Liu, 2017. "Analysis of Dynamic Characteristic for Solar Arrays in Series and Global Maximum Power Point Tracking Based on Optimal Initial Value Incremental Conductance Strategy under Partially Shaded Conditions," Energies, MDPI, vol. 10(1), pages 1-23, January.
    2. Pichel, N. & Vivar, M. & Fuentes, M., 2021. "Comparative analysis of the SolWat photovoltaic performance regarding different PV technologies and hydraulic retention times," Applied Energy, Elsevier, vol. 292(C).
    3. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    4. Hashemzadeh, Seyed Majid, 2019. "A new model-based technique for fast and accurate tracking of global maximum power point in photovoltaic arrays under partial shading conditions," Renewable Energy, Elsevier, vol. 139(C), pages 1061-1076.
    5. Kuei-Hsiang Chao & Muhammad Nursyam Rizal, 2021. "A Hybrid MPPT Controller Based on the Genetic Algorithm and Ant Colony Optimization for Photovoltaic Systems under Partially Shaded Conditions," Energies, MDPI, vol. 14(10), pages 1-17, May.
    6. Shen, Yongting & Hocksun Kwan, Trevor & Yang, Hongxing, 2022. "Parametric and global seasonal analysis of a hybrid PV/T-CCA system for combined CO2 capture and power generation," Applied Energy, Elsevier, vol. 311(C).
    7. Zhang, Xiaoshun & Li, Shengnan & He, Tingyi & Yang, Bo & Yu, Tao & Li, Haofei & Jiang, Lin & Sun, Liming, 2019. "Memetic reinforcement learning based maximum power point tracking design for PV systems under partial shading condition," Energy, Elsevier, vol. 174(C), pages 1079-1090.
    8. Chen, Zhicong & Wu, Lijun & Lin, Peijie & Wu, Yue & Cheng, Shuying, 2016. "Parameters identification of photovoltaic models using hybrid adaptive Nelder-Mead simplex algorithm based on eagle strategy," Applied Energy, Elsevier, vol. 182(C), pages 47-57.
    9. 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.
    10. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    11. Rizzo, Santi Agatino & Scelba, Giacomo, 2015. "ANN based MPPT method for rapidly variable shading conditions," Applied Energy, Elsevier, vol. 145(C), pages 124-132.
    12. 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.
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    1. Belqasem Aljafari & Priya Ranjan Satpathy & Siva Rama Krishna Madeti & Pradeep Vishnuram & Sudhakar Babu Thanikanti, 2022. "Reliability Enhancement of Photovoltaic Systems under Partial Shading through a Two-Step Module Placement Approach," Energies, MDPI, vol. 15(20), pages 1-27, October.

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