IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i4p852-d139706.html
   My bibliography  Save this article

Shading Ratio Impact on Photovoltaic Modules and Correlation with Shading Patterns

Author

Listed:
  • Alonso Gutiérrez Galeano

    (Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá 111711, Colombia
    Doctoral School GEET, Université Toulouse III, F-31400 Toulouse, France)

  • Michael Bressan

    (Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá 111711, Colombia)

  • Fernando Jiménez Vargas

    (Department of Electrical and Electronic Engineering, Universidad de los Andes, Bogotá 111711, Colombia
    LAAS-CNRS, 7 Avenue du Colonel Roche, F-31077 Toulouse, France)

  • Corinne Alonso

    (Doctoral School GEET, Université Toulouse III, F-31400 Toulouse, France
    LAAS-CNRS, 7 Avenue du Colonel Roche, F-31077 Toulouse, France)

Abstract

This paper presents the study of a simplified approach to model and analyze the performance of partially shaded photovoltaic modules using the shading ratio. This approach integrates the characteristics of shaded area and shadow opacity into the photovoltaic cell model. The studied methodology is intended to improve the description of shaded photovoltaic systems by specifying an experimental procedure to quantify the shadow impact. Furthermore, with the help of image processing, the analysis of the shading ratio provides a set of rules useful for predicting the current–voltage behavior and the maximum power points of shaded photovoltaic modules. This correlation of the shading ratio and shading patterns can contribute to the supervision of actual photovoltaic installations. The experimental results validate the proposed approach in monocrystalline and polycrystalline technologies of solar panels.

Suggested Citation

  • Alonso Gutiérrez Galeano & Michael Bressan & Fernando Jiménez Vargas & Corinne Alonso, 2018. "Shading Ratio Impact on Photovoltaic Modules and Correlation with Shading Patterns," Energies, MDPI, vol. 11(4), pages 1-26, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:852-:d:139706
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/4/852/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/4/852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. S. Zahra Mirbagheri Golroodbari & Arjen. C. De Waal & Wilfried G. J. H. M. Van Sark, 2018. "Improvement of Shade Resilience in Photovoltaic Modules Using Buck Converters in a Smart Module Architecture," Energies, MDPI, vol. 11(1), pages 1-19, January.
    3. Qiang Zhao & Shuai Shao & Lingxing Lu & Xin Liu & Honglu Zhu, 2018. "A New PV Array Fault Diagnosis Method Using Fuzzy C-Mean Clustering and Fuzzy Membership Algorithm," Energies, MDPI, vol. 11(1), pages 1-21, January.
    4. d'Alessandro, Vincenzo & Di Napoli, Fabio & Guerriero, Pierluigi & Daliento, Santolo, 2015. "An automated high-granularity tool for a fast evaluation of the yield of PV plants accounting for shading effects," Renewable Energy, Elsevier, vol. 83(C), pages 294-304.
    5. Kadri, Riad & Andrei, Horia & Gaubert, Jean-Paul & Ivanovici, Traian & Champenois, Gérard & Andrei, Paul, 2012. "Modeling of the photovoltaic cell circuit parameters for optimum connection model and real-time emulator with partial shadow conditions," Energy, Elsevier, vol. 42(1), pages 57-67.
    6. Bressan, M. & El Basri, Y. & Galeano, A.G. & Alonso, C., 2016. "A shadow fault detection method based on the standard error analysis of I-V curves," Renewable Energy, Elsevier, vol. 99(C), pages 1181-1190.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grzegorz Ostasz & Dominika Siwiec & Andrzej Pacana, 2022. "Universal Model to Predict Expected Direction of Products Quality Improvement," Energies, MDPI, vol. 15(5), pages 1-18, February.
    2. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    3. Mansouri, Seyed Amir & Nematbakhsh, Emad & Ahmarinejad, Amir & Jordehi, Ahmad Rezaee & Javadi, Mohammad Sadegh & Marzband, Mousa, 2022. "A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Hao Wu & Lin Zhou & Yihao Wan & Qiang Liu & Siyu Zhou, 2019. "A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster," Energies, MDPI, vol. 12(20), pages 1-16, October.
    5. Bojan Kranjec & Sasa Sladic & Wojciech Giernacki & Neven Bulic, 2018. "PV System Design and Flight Efficiency Considerations for Fixed-Wing Radio-Controlled Aircraft—A Case Study," Energies, MDPI, vol. 11(10), pages 1-12, October.
    6. Catarina Pinho Correia Valério Bernardo & Ricardo A. Marques Lameirinhas & João Paulo Neto Torres & António Baptista, 2023. "The Shading Influence on the Economic Viability of a Real Photovoltaic System Project," Energies, MDPI, vol. 16(6), pages 1-17, March.
    7. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.
    8. Abdulhamid Atia & Fatih Anayi & Min Gao, 2022. "Influence of Shading on Solar Cell Parameters and Modelling Accuracy Improvement of PV Modules with Reverse Biased Solar Cells," Energies, MDPI, vol. 15(23), pages 1-19, November.
    9. Ahmed Al Mansur & Md. Ruhul Amin & Kazi Khairul Islam, 2019. "Performance Comparison of Mismatch Power Loss Minimization Techniques in Series-Parallel PV Array Configurations," Energies, MDPI, vol. 12(5), pages 1-21, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rico Espinosa, Alejandro & Bressan, Michael & Giraldo, Luis Felipe, 2020. "Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 249-256.
    2. Bressan, M. & Gutierrez, A. & Garcia Gutierrez, L. & Alonso, C., 2018. "Development of a real-time hot-spot prevention using an emulator of partially shaded PV systems," Renewable Energy, Elsevier, vol. 127(C), pages 334-343.
    3. Bojan Kranjec & Sasa Sladic & Wojciech Giernacki & Neven Bulic, 2018. "PV System Design and Flight Efficiency Considerations for Fixed-Wing Radio-Controlled Aircraft—A Case Study," Energies, MDPI, vol. 11(10), pages 1-12, October.
    4. Yadav, Anurag Singh & Mukherjee, V., 2021. "Conventional and advanced PV array configurations to extract maximum power under partial shading conditions: A review," Renewable Energy, Elsevier, vol. 178(C), pages 977-1005.
    5. Belhaouas, N. & Cheikh, M.-S. Ait & Agathoklis, P. & Oularbi, M.-R. & Amrouche, B. & Sedraoui, K. & Djilali, N., 2017. "PV array power output maximization under partial shading using new shifted PV array arrangements," Applied Energy, Elsevier, vol. 187(C), pages 326-337.
    6. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    7. Hao Wu & Lin Zhou & Yihao Wan & Qiang Liu & Siyu Zhou, 2019. "A Mixed Uncertainty Power Flow Algorithm-Based Centralized Photovoltaic (PV) Cluster," Energies, MDPI, vol. 12(20), pages 1-16, October.
    8. Gao, Dan & Zhao, Yang & Liang, Kai & He, Shuyu & Zhang, Heng & Chen, Haiping, 2022. "Energy and exergy analyses of a low-concentration photovoltaic/thermal module with glass channel," Energy, Elsevier, vol. 253(C).
    9. Uche, J. & Círez, F. & Bayod, A.A. & Martínez, A., 2013. "On-grid and off-grid batch-ED (electrodialysis) process: Simulation and experimental tests," Energy, Elsevier, vol. 57(C), pages 44-54.
    10. Jordehi, A. Rezaee, 2016. "Parameter estimation of solar photovoltaic (PV) cells: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 354-371.
    11. Santiago Pindado & Javier Cubas & Elena Roibás-Millán & Francisco Bugallo-Siegel & Félix Sorribes-Palmer, 2018. "Assessment of Explicit Models for Different Photovoltaic Technologies," Energies, MDPI, vol. 11(6), pages 1-22, May.
    12. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Mather, Peter, 2017. "Photovoltaic fault detection algorithm based on theoretical curves modelling and fuzzy classification system," Energy, Elsevier, vol. 140(P1), pages 276-290.
    13. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    14. Ranjbaran, Parisa & Yousefi, Hossein & Gharehpetian, G.B. & Astaraei, Fatemeh Razi, 2019. "A review on floating photovoltaic (FPV) power generation units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 332-347.
    15. Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. van Sark, 2019. "A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-18, May.
    16. Dan Craciunescu & Laurentiu Fara, 2023. "Investigation of the Partial Shading Effect of Photovoltaic Panels and Optimization of Their Performance Based on High-Efficiency FLC Algorithm," Energies, MDPI, vol. 16(3), pages 1-28, January.
    17. Slimane Hadji & Jean-Paul Gaubert & Fateh Krim, 2018. "Real-Time Genetic Algorithms-Based MPPT: Study and Comparison (Theoretical an Experimental) with Conventional Methods," Energies, MDPI, vol. 11(2), pages 1-17, February.
    18. Carlos Cárdenas-Bravo & Rodrigo Barraza & Antonio Sánchez-Squella & Patricio Valdivia-Lefort & Federico Castillo-Burns, 2021. "Estimation of Single-Diode Photovoltaic Model Using the Differential Evolution Algorithm with Adaptive Boundaries," Energies, MDPI, vol. 14(13), pages 1-24, June.
    19. Hyunji Lee & Katherine A. Kim, 2018. "Design Considerations for Parallel Differential Power Processing Converters in a Photovoltaic-Powered Wearable Application," Energies, MDPI, vol. 11(12), pages 1-17, November.
    20. Li, Shaowu, 2016. "Linear equivalent models at the maximum power point based on variable weather parameters for photovoltaic cell," Applied Energy, Elsevier, vol. 182(C), pages 94-104.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:852-:d:139706. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.