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Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition

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  • Pal, Rudra Sankar
  • Mukherjee, V.

Abstract

The characteristic of the photovoltaic (PV) system during partial shading condition comprises of one global peak and multiple local peaks. It is, therefore, very difficult to track maximum power from the PV arrays. Traditional maximum power point (MPP) tracking (MPPT) algorithms are commonly limited to uniform irradiance condition. In this manuscript, the problem under study is the tracking of maximum power from a PV array in real-time system. Consequently, this paper proposes an improved chaotic PSO (CPSO) (ICPSO) for extracting maximum power from the PV array under various environmental conditions. In the algorithm, chaotic mutation is engrafted to overcome trapping of normal PSO into local MPPs. Moreover, tracking time, number of iteration and efficiency are also improved considerably by the proposed algorithm. ICPSO based simulation results under four different irradiance patterns for each PV array configuration (such as 3S1P and 4S2P) are verified against PSO, improved PSO, CPSO, cuckoo search, and perturb and observed algorithm. The obtained results also ensure that the tracking efficiency of the proposed technique is better than the other approaches in most of the cases, which leads better outlook to use this technique in the control block for searching the global MPP of the PV setup.

Suggested Citation

  • Pal, Rudra Sankar & Mukherjee, V., 2020. "Metaheuristic based comparative MPPT methods for photovoltaic technology under partial shading condition," Energy, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s036054422031700x
    DOI: 10.1016/j.energy.2020.118592
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    References listed on IDEAS

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    Cited by:

    1. Hou, Guolian & Ke, Yin & Huang, Congzhi, 2021. "A flexible constant power generation scheme for photovoltaic system by error-based active disturbance rejection control and perturb & observe," Energy, Elsevier, vol. 237(C).
    2. Liu, Xiangjie & Zhu, Zheng & Kong, Xiaobing & Ma, Lele & Lee, Kwang Y., 2023. "An economic model predictive control-based flexible power point tracking strategy for photovoltaic power generation," Energy, Elsevier, vol. 283(C).
    3. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    4. Mostafa Bakkar & Ahmed Aboelhassan & Mostafa Abdelgeliel & Michael Galea, 2021. "PV Systems Control Using Fuzzy Logic Controller Employing Dynamic Safety Margin under Normal and Partial Shading Conditions," Energies, MDPI, vol. 14(4), pages 1-20, February.
    5. Celikel, Resat & Yilmaz, Musa & Gundogdu, Ahmet, 2022. "A voltage scanning-based MPPT method for PV power systems under complex partial shading conditions," Renewable Energy, Elsevier, vol. 184(C), pages 361-373.
    6. Khaled Osmani & Ahmad Haddad & Mohammad Alkhedher & Thierry Lemenand & Bruno Castanier & Mohamad Ramadan, 2023. "A Novel MPPT-Based Lithium-Ion Battery Solar Charger for Operation under Fluctuating Irradiance Conditions," Sustainability, MDPI, vol. 15(12), pages 1-31, June.
    7. Luz Adriana Trejos-Grisales & Juan David Bastidas-Rodríguez & Carlos Andrés Ramos-Paja, 2020. "Mathematical Model for Regular and Irregular PV Arrays with Improved Calculation Speed," Sustainability, MDPI, vol. 12(24), pages 1-28, December.
    8. Mirza, Adeel Feroz & Mansoor, Majad & Zhan, Keyu & Ling, Qiang, 2021. "High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance," Energy, Elsevier, vol. 228(C).

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