IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v89y2018icp88-98.html
   My bibliography  Save this article

A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems

Author

Listed:
  • Lu, Shibo
  • Phung, B.T.
  • Zhang, Daming

Abstract

Integration of renewable energy including solar energy is growing faster than ever before. Solar energy supplies more than 1.3% of global power, and it is predicted to become the largest electricity source by 2050 with about 11% of global power consumption. However, the improper installation, non-frequently scheduled maintenance, and aging effect can accelerate the deterioration of PV system components, which directly increase the possibility of arc fault occurrence. The undetected arc faults pose a severe fire hazard to residential, commercial, and utility-scaled PV systems. To deliver electricity in a safe and reliable manner, such a dangerous event must be detected at early stage. This paper presents a comprehensive review of the-state-of-art techniques for DC arc faults detection in photovoltaic systems (PV). Different methods and the features used for detection are discussed and compared in detail. This paper also emphasizes the importance of DC arc fault simulation for characteristics study and fault diagnosis purpose. Several DC arc fault models have been reviewed and compared.

Suggested Citation

  • Lu, Shibo & Phung, B.T. & Zhang, Daming, 2018. "A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 88-98.
    • Handle: RePEc:eee:rensus:v:89:y:2018:i:c:p:88-98
    DOI: 10.1016/j.rser.2018.03.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032118300996
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2018.03.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cotfas, D.T. & Cotfas, P.A. & Kaplanis, S., 2016. "Methods and techniques to determine the dynamic parameters of solar cells: Review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 213-221.
    2. 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.
    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. Xu, Wenqiang & Wu, Xiaogang & Li, Yalun & Wang, Hewu & Lu, Languang & Ouyang, Minggao, 2023. "A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
    2. Yao Wang & Cuiyan Bai & Xiaopeng Qian & Wanting Liu & Chen Zhu & Leijiao Ge, 2022. "A DC Series Arc Fault Detection Method Based on a Lightweight Convolutional Neural Network Used in Photovoltaic System," Energies, MDPI, vol. 15(8), pages 1-20, April.
    3. Lina Wang & Ehtisham Lodhi & Pu Yang & Hongcheng Qiu & Waheed Ur Rehman & Zeeshan Lodhi & Tariku Sinshaw Tamir & M. Adil Khan, 2022. "Adaptive Local Mean Decomposition and Multiscale-Fuzzy Entropy-Based Algorithms for the Detection of DC Series Arc Faults in PV Systems," Energies, MDPI, vol. 15(10), pages 1-16, May.
    4. Lina Wang & Hongcheng Qiu & Pu Yang & Longhua Mu, 2021. "Arc Fault Detection Algorithm Based on Variational Mode Decomposition and Improved Multi-Scale Fuzzy Entropy," Energies, MDPI, vol. 14(14), pages 1-16, July.
    5. Teng Li & Zhijie Jiao & Lina Wang & Yong Mu, 2020. "A Method of DC Arc Detection in All-Electric Aircraft," Energies, MDPI, vol. 13(16), pages 1-14, August.
    6. 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).
    7. Khairul Eahsun Fahim & Liyanage C. De Silva & Fayaz Hussain & Hayati Yassin, 2023. "A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations," Sustainability, MDPI, vol. 15(15), pages 1-29, August.
    8. Lei Song & Chunguang Lu & Chen Li & Yongjin Xu & Lin Liu & Xianbo Wang, 2024. "Progress of Photovoltaic DC Fault Arc Detection Based on VOSviewer Bibliometric Analysis," Energies, MDPI, vol. 17(11), pages 1-17, May.
    9. Zahid Javid & Ilhan Kocar & William Holderbaum & Ulas Karaagac, 2024. "Future Distribution Networks: A Review," Energies, MDPI, vol. 17(8), pages 1-45, April.
    10. Yaseen Ahmed Mohammed Alsumaidaee & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Kharudin Ali, 2022. "Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning," Energies, MDPI, vol. 15(18), pages 1-34, September.
    11. Arturo Y. Jaen-Cuellar & David A. Elvira-Ortiz & Roque A. Osornio-Rios & Jose A. Antonino-Daviu, 2022. "Advances in Fault Condition Monitoring for Solar Photovoltaic and Wind Turbine Energy Generation: A Review," Energies, MDPI, vol. 15(15), pages 1-36, July.

    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. Vorachack Kongphet & Anne Migan-Dubois & Claude Delpha & Jean-Yves Lechenadec & Demba Diallo, 2022. "Low-Cost I–V Tracer for PV Fault Diagnosis Using Single-Diode Model Parameters and I–V Curve Characteristics," Energies, MDPI, vol. 15(15), pages 1-31, July.
    2. Madi, Saida & Kheldoun, Aissa, 2017. "Bond graph based modeling for parameter identification of photovoltaic module," Energy, Elsevier, vol. 141(C), pages 1456-1465.
    3. Tong Kang & Jiangang Yao & Min Jin & Shengjie Yang & ThanhLong Duong, 2018. "A Novel Improved Cuckoo Search Algorithm for Parameter Estimation of Photovoltaic (PV) Models," Energies, MDPI, vol. 11(5), pages 1-31, April.
    4. Tao, Yunkun & Bai, Jianbo & Pachauri, Rupendra Kumar & Wang, Yue & Li, Jian & Attaher, Harouna Kerzika, 2021. "Parameterizing mismatch loss in bifacial photovoltaic modules with global deployment: A comprehensive study," Applied Energy, Elsevier, vol. 303(C).
    5. Abbassi, Rabeh & Abbassi, Abdelkader & Jemli, Mohamed & Chebbi, Souad, 2018. "Identification of unknown parameters of solar cell models: A comprehensive overview of available approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 453-474.
    6. Efstratios Batzelis, 2019. "Non-Iterative Methods for the Extraction of the Single-Diode Model Parameters of Photovoltaic Modules: A Review and Comparative Assessment," Energies, MDPI, vol. 12(3), pages 1-26, January.
    7. Tamer Khatib & Dhiaa Halboot Muhsen, 2020. "Optimal Sizing of Standalone Photovoltaic System Using Improved Performance Model and Optimization Algorithm," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    8. Senturk, Ali, 2020. "Investigation of datasheet provided temperature coefficients of photovoltaic modules under various sky profiles at the field by applying a new validation procedure," Renewable Energy, Elsevier, vol. 152(C), pages 644-652.
    9. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    10. Arshdeep Singh & Shimi Sudha Letha, 2019. "Emerging energy sources for electric vehicle charging station," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(5), pages 2043-2082, October.
    11. Guohong Lai & Guoping Zhang & Shaowu Li, 2024. "An MPPT Control Strategy Based on Current Constraint Relationships for a Photovoltaic System with a Battery or Supercapacitor," Energies, MDPI, vol. 17(16), pages 1-27, August.
    12. Tihomir Betti & Ante Kristić & Ivan Marasović & Vesna Pekić, 2024. "Accuracy of Simscape Solar Cell Block for Modeling a Partially Shaded Photovoltaic Module," Energies, MDPI, vol. 17(10), pages 1-19, May.
    13. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    14. Pillai, Dhanup S. & Rajasekar, N., 2018. "Metaheuristic algorithms for PV parameter identification: A comprehensive review with an application to threshold setting for fault detection in PV systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3503-3525.
    15. Issa, Mohamed & Samn, Anas, 2022. "Passive vehicle suspension system optimization using Harris Hawk Optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 328-345.
    16. 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.
    17. Petru Adrian Cotfas & Daniel Tudor Cotfas & Paul Nicolae Borza & Dezso Sera & Remus Teodorescu, 2018. "Solar Cell Capacitance Determination Based on an RLC Resonant Circuit," Energies, MDPI, vol. 11(3), pages 1-13, March.
    18. Hu, Mingke & Zhao, Bin & Li, Jing & Wang, Yunyun & Pei, Gang, 2017. "Preliminary thermal analysis of a combined photovoltaic–photothermic–nocturnal radiative cooling system," Energy, Elsevier, vol. 137(C), pages 419-430.
    19. Martin Ćalasan & Dražen Jovanović & Vesna Rubežić & Saša Mujović & Slobodan Đukanović, 2019. "Estimation of Single-Diode and Two-Diode Solar Cell Parameters by Using a Chaotic Optimization Approach," Energies, MDPI, vol. 12(21), pages 1-14, November.
    20. Li, Baojie & Hansen, Clifford W. & Chen, Xin & Diallo, Demba & Migan-Dubois, Anne & Delpha, Claude & Jain, Anubhav, 2024. "A robust I–V curve correction procedure for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 224(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:eee:rensus:v:89:y:2018:i:c:p:88-98. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.