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Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm

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  • Fathy, Ahmed

Abstract

The operation and performance of a photovoltaic system (PV) are affected by some factors such as; solar radiation, ambient temperature, PV array configuration and shadow which may be either completely or partially. The partially shadow is caused by clouds, trees due to wind, neighboring buildings and utilities. The shadow effect causes the multiple local maximum power points in the PV module voltage-power characteristics and only one Global Maximum Power Point (GMPP); additionally the shadowing causes high power loss in the shaded cells and produces hot spot. In this paper a new optimization approach based on proposed Modified Artificial Bee Colony (MABC) algorithm is used to solve a proposed constrained objective function of PV module power loss and mitigate the shading effect. The proposed MABC is compared with GA, PSO and ABC. The obtained results proved that the MABC is the most efficient algorithm in solving the objective function that mitigating the power loss in the PV module under partially shading effect.

Suggested Citation

  • Fathy, Ahmed, 2015. "Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm," Renewable Energy, Elsevier, vol. 81(C), pages 78-88.
    • Handle: RePEc:eee:renene:v:81:y:2015:i:c:p:78-88
    DOI: 10.1016/j.renene.2015.03.017
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    Citations

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    Cited by:

    1. Ali M. Eltamaly & Hassan M. H. Farh & Mamdooh S. Al Saud, 2019. "Impact of PSO Reinitialization on the Accuracy of Dynamic Global Maximum Power Detection of Variant Partially Shaded PV Systems," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    2. 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.
    3. Zakaria Belboul & Belgacem Toual & Abdellah Kouzou & Lakhdar Mokrani & Abderrahman Bensalem & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria," Energies, MDPI, vol. 15(10), pages 1-30, May.
    4. Saravanan, S. & Ramesh Babu, N., 2016. "Maximum power point tracking algorithms for photovoltaic system – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 192-204.
    5. Ahmad, R. & Murtaza, Ali F. & Ahmed Sher, Hadeed & Tabrez Shami, Umar & Olalekan, Saheed, 2017. "An analytical approach to study partial shading effects on PV array supported by literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 721-732.
    6. Fahd A. Alturki & Abdullrahman A. Al-Shamma’a & Hassan M. H. Farh, 2020. "Simulations and dSPACE Real-Time Implementation of Photovoltaic Global Maximum Power Extraction under Partial Shading," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    7. Jena, Debashisha & Ramana, Vanjari Venkata, 2015. "Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 400-417.
    8. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2016. "Enhancing the design of battery charging controllers for photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 646-655.
    9. Koohi-Kamalі, Sam & Rahim, N.A. & Mokhlis, H. & Tyagi, V.V., 2016. "Photovoltaic electricity generator dynamic modeling methods for smart grid applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 131-172.
    10. Bin Huang & Jialiang Huang & Ke Xing & Lida Liao & Peiling Xie & Meng Xiao & Wei Zhao, 2023. "Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis," Energies, MDPI, vol. 16(10), pages 1-19, May.
    11. Carlos Robles Algarín & John Taborda Giraldo & Omar Rodríguez Álvarez, 2017. "Fuzzy Logic Based MPPT Controller for a PV System," Energies, MDPI, vol. 10(12), pages 1-18, December.
    12. Dhimish, Mahmoud & Holmes, Violeta & Dales, Mark, 2017. "Parallel fault detection algorithm for grid-connected photovoltaic plants," Renewable Energy, Elsevier, vol. 113(C), pages 94-111.
    13. Belhachat, Faiza & Larbes, Cherif, 2017. "Global maximum power point tracking based on ANFIS approach for PV array configurations under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 875-889.
    14. Somashree Pathy & C. Subramani & R. Sridhar & T. M. Thamizh Thentral & Sanjeevikumar Padmanaban, 2019. "Nature-Inspired MPPT Algorithms for Partially Shaded PV Systems: A Comparative Study," Energies, MDPI, vol. 12(8), pages 1-21, April.
    15. Fathy, Ahmed, 2016. "A reliable methodology based on mine blast optimization algorithm for optimal sizing of hybrid PV-wind-FC system for remote area in Egypt," Renewable Energy, Elsevier, vol. 95(C), pages 367-380.
    16. Abu Eldahab, Yasser E. & Saad, Naggar H. & Zekry, Abdalhalim, 2017. "Enhancing the tracking techniques for the global maximum power point under partial shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1173-1183.
    17. Li, Guiqiang & Jin, Yi & Akram, M.W. & Chen, Xiao & Ji, Jie, 2018. "Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 840-873.

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