IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i9p2397-d541888.html
   My bibliography  Save this article

Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach

Author

Listed:
  • Luis D. Murillo-Soto

    (School of Electromechanical Engineering, Costa Rica Institute of Technology, Cartago 30101, Costa Rica
    Costa Rica Institute of Technology, Costa Rica.
    These authors contributed equally to this work.)

  • Carlos Meza

    (School of Electronic Engineering, Costa Rica Institute of Technology, Cartago 30101, Costa Rica
    Costa Rica Institute of Technology, Costa Rica.
    These authors contributed equally to this work.)

Abstract

This work proposes an automated reconfiguration system to manage two types of faults in any position inside the solar arrays. The faults studied are the short-circuit to ground and the open wires in the string. These faults were selected because they severely affect power production. By identifying the affected panels and isolating the faulty one, it is possible to recover part of the power loss. Among other types of faults that the system can detect and locate are: diode short-circuit, internal open-circuit, and the degradation of the internal parasitic serial resistance. The reconfiguration system can detect, locate the above faults, and switch the distributed commutators to recover most of the power loss. Moreover, the system can return automatically to the previous state when the fault has been repaired. A SIMULINK model has been built to prove this automatic system, and a simulated numerical experiment has been executed to test the system response to the faults mentioned. The results show that the recovery of power is more than 90%, and the diagnosis accuracy and sensitivity are both 100% for this numerical experiment.

Suggested Citation

  • Luis D. Murillo-Soto & Carlos Meza, 2021. "Automated Fault Management System in a Photovoltaic Array: A Reconfiguration-Based Approach," Energies, MDPI, vol. 14(9), pages 1-19, April.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2397-:d:541888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/9/2397/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/9/2397/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Deshkar, Shubhankar Niranjan & Dhale, Sumedh Bhaskar & Mukherjee, Jishnu Shekar & Babu, T. Sudhakar & Rajasekar, N., 2015. "Solar PV array reconfiguration under partial shading conditions for maximum power extraction using genetic algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 102-110.
    3. 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.
    4. Dhimish, Mahmoud & Holmes, Violeta & Mehrdadi, Bruce & Dales, Mark & Chong, Benjamin & Zhang, Li, 2017. "Seven indicators variations for multiple PV array configurations under partial shading and faulty PV conditions," Renewable Energy, Elsevier, vol. 113(C), pages 438-460.
    5. Saeedreza Jadidi & Hamed Badihi & Youmin Zhang, 2020. "Passive Fault-Tolerant Control Strategies for Power Converter in a Hybrid Microgrid," Energies, MDPI, vol. 13(21), pages 1-28, October.
    6. Malathy, S. & Ramaprabha, R., 2018. "Reconfiguration strategies to extract maximum power from photovoltaic array under partially shaded conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2922-2934.
    7. Dong Ji & Cai Zhang & Mingsong Lv & Ye Ma & Nan Guan, 2017. "Photovoltaic Array Fault Detection by Automatic Reconfiguration," Energies, MDPI, vol. 10(5), pages 1-13, May.
    8. Chine, W. & Mellit, A. & Lughi, V. & Malek, A. & Sulligoi, G. & Massi Pavan, A., 2016. "A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks," Renewable Energy, Elsevier, vol. 90(C), pages 501-512.
    9. Giuseppe Schettino & Filippo Pellitteri & Guido Ala & Rosario Miceli & Pietro Romano & Fabio Viola, 2020. "Dynamic Reconfiguration Systems for PV Plant: Technical and Economic Analysis," Energies, MDPI, vol. 13(8), pages 1-21, April.
    Full references (including those not matched with items on IDEAS)

    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. Rezk, Hegazy & AL-Oran, Mazen & Gomaa, Mohamed R. & Tolba, Mohamed A. & Fathy, Ahmed & Abdelkareem, Mohammad Ali & Olabi, A.G. & El-Sayed, Abou Hashema M., 2019. "A novel statistical performance evaluation of most modern optimization-based global MPPT techniques for partially shaded PV system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    2. Li, Yuanliang & Ding, Kun & Zhang, Jingwei & Chen, Fudong & Chen, Xiang & Wu, Jiabing, 2019. "A fault diagnosis method for photovoltaic arrays based on fault parameters identification," Renewable Energy, Elsevier, vol. 143(C), pages 52-63.
    3. 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.
    4. Aljafari, Belqasem & Satpathy, Priya Ranjan & Thanikanti, Sudhakar Babu, 2022. "Partial shading mitigation in PV arrays through dragonfly algorithm based dynamic reconfiguration," Energy, Elsevier, vol. 257(C).
    5. Čabo, Filip Grubišić & Marinić-Kragić, Ivo & Garma, Tonko & Nižetić, Sandro, 2021. "Development of thermo-electrical model of photovoltaic panel under hot-spot conditions with experimental validation," Energy, Elsevier, vol. 230(C).
    6. 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.
    7. Nien-Che Yang & Harun Ismail, 2022. "Voting-Based Ensemble Learning Algorithm for Fault Detection in Photovoltaic Systems under Different Weather Conditions," Mathematics, MDPI, vol. 10(2), pages 1-18, January.
    8. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2023. "Cost-effective fault diagnosis of nearby photovoltaic systems using graph neural networks," Energy, Elsevier, vol. 266(C).
    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.
    10. Belaout, A. & Krim, F. & Mellit, A. & Talbi, B. & Arabi, A., 2018. "Multiclass adaptive neuro-fuzzy classifier and feature selection techniques for photovoltaic array fault detection and classification," Renewable Energy, Elsevier, vol. 127(C), pages 548-558.
    11. Pillai, Dhanup S. & Rajasekar, N., 2018. "A comprehensive review on protection challenges and fault diagnosis in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 18-40.
    12. Livera, Andreas & Theristis, Marios & Makrides, George & Georghiou, George E., 2019. "Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems," Renewable Energy, Elsevier, vol. 133(C), pages 126-143.
    13. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    14. Heinrich, Matthias & Meunier, Simon & Samé, Allou & Quéval, Loïc & Darga, Arouna & Oukhellou, Latifa & Multon, Bernard, 2020. "Detection of cleaning interventions on photovoltaic modules with machine learning," Applied Energy, Elsevier, vol. 263(C).
    15. Pillai, Dhanup S. & Ram, J. Prasanth & Shabunko, Veronika & Kim, Young-Jin, 2021. "A new shade dispersion technique compatible for symmetrical and unsymmetrical photovoltaic (PV) arrays," Energy, Elsevier, vol. 225(C).
    16. Mellit, Adel & Kalogirou, Soteris, 2021. "Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    17. Joshuva Arockia Dhanraj & Ali Mostafaeipour & Karthikeyan Velmurugan & Kuaanan Techato & Prem Kumar Chaurasiya & Jenoris Muthiya Solomon & Anitha Gopalan & Khamphe Phoungthong, 2021. "An Effective Evaluation on Fault Detection in Solar Panels," Energies, MDPI, vol. 14(22), pages 1-14, November.
    18. Selma Tchoketch Kebir & Nawal Cheggaga & Adrian Ilinca & Sabri Boulouma, 2021. "An Efficient Neural Network-Based Method for Diagnosing Faults of PV Array," Sustainability, MDPI, vol. 13(11), pages 1-27, May.
    19. Wang, Haizheng & Zhao, Jian & Sun, Qian & Zhu, Honglu, 2019. "Probability modeling for PV array output interval and its application in fault diagnosis," Energy, Elsevier, vol. 189(C).
    20. Van Gompel, Jonas & Spina, Domenico & Develder, Chris, 2022. "Satellite based fault diagnosis of photovoltaic systems using recurrent neural networks," Applied Energy, Elsevier, vol. 305(C).

    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:14:y:2021:i:9:p:2397-:d:541888. 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.