IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i5p795-d209547.html
   My bibliography  Save this article

Monitoring of Defects of a Photovoltaic Power Plant Using a Drone

Author

Listed:
  • Martin Libra

    (Faculty of Engineering; Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Milan Daneček

    (Faculty of Engineering; Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Jan Lešetický

    (Faculty of Engineering; Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Vladislav Poulek

    (Faculty of Engineering; Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Jan Sedláček

    (Faculty of Engineering; Czech University of Life Sciences Prague, Kamycka 129, 16500 Prague, Czech Republic)

  • Václav Beránek

    (Solarmonitoring, Ltd., 14700 Prague, Czech Republic)

Abstract

Drone infrared camera monitoring of photovoltaic (PV) power plants allows us to quickly see a large area and to find the worst defects in PV panels, namely cracked PV cells with broken contacts. Roofs are suitable for the integration of PV power plants into buildings. The power plant at the Czech University of Life Sciences in Prague, which was monitored by this method, does not show any significant defects, and the produced electric energy exceeds the expected values. On the contrary, the PV power plant in Ladná has visible defects, and the data monitoring system Solarmon-2.0 also indicates defects. Our newly developed data monitoring system Solarmon-2.0 has been successfully used in 65 PV power plants in the Czech Republic and in many PV power plants throughout the world. Data are archived and interpreted in our dispatch area at the Czech University of Life Sciences in Prague. The monitoring system can report possible failure(s) if the measured amount of energy differs from the expected value(s). The relation of the measured values of PV power to the PV panel temperature is justified, which is consistent with the physical theory of semiconductors.

Suggested Citation

  • Martin Libra & Milan Daneček & Jan Lešetický & Vladislav Poulek & Jan Sedláček & Václav Beránek, 2019. "Monitoring of Defects of a Photovoltaic Power Plant Using a Drone," Energies, MDPI, vol. 12(5), pages 1-9, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:795-:d:209547
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/5/795/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/5/795/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Harrou, Fouzi & Sun, Ying & Taghezouit, Bilal & Saidi, Ahmed & Hamlati, Mohamed-Elkarim, 2018. "Reliable fault detection and diagnosis of photovoltaic systems based on statistical monitoring approaches," Renewable Energy, Elsevier, vol. 116(PA), pages 22-37.
    2. Eduardo Manuel Godinho Rodrigues & Radu Godina & Mousa Marzband & Edris Pouresmaeil, 2018. "Simulation and Comparison of Mathematical Models of PV Cells with Growing Levels of Complexity," Energies, MDPI, vol. 11(11), pages 1-21, October.
    3. Samantha Wermager & Stuart Baur, 2013. "Energy Analysis of a Student-Designed Solar House," Energies, MDPI, vol. 6(12), pages 1-18, December.
    4. Madeti, Siva Ramakrishna & Singh, S.N., 2017. "Monitoring system for photovoltaic plants: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 1180-1207.
    5. ASM Shihavuddin & Xiao Chen & Vladimir Fedorov & Anders Nymark Christensen & Nicolai Andre Brogaard Riis & Kim Branner & Anders Bjorholm Dahl & Rasmus Reinhold Paulsen, 2019. "Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis," Energies, MDPI, vol. 12(4), pages 1-15, February.
    6. Moath Alsafasfeh & Ikhlas Abdel-Qader & Bradley Bazuin & Qais Alsafasfeh & Wencong Su, 2018. "Unsupervised Fault Detection and Analysis for Large Photovoltaic Systems Using Drones and Machine Vision," Energies, MDPI, vol. 11(9), pages 1-18, August.
    7. Emily Prehoda & Joshua M. Pearce & Chelsea Schelly, 2019. "Policies to Overcome Barriers for Renewable Energy Distributed Generation: A Case Study of Utility Structure and Regulatory Regimes in Michigan," Energies, MDPI, vol. 12(4), pages 1-23, February.
    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. Myeongchan Oh & Hyeong-Dong Park, 2019. "Optimization of Solar Panel Orientation Considering Temporal Volatility and Scenario-Based Photovoltaic Potential: A Case Study in Seoul National University," Energies, MDPI, vol. 12(17), pages 1-17, August.
    2. Pavel Kuznetsov & Dmitry Kotelnikov & Leonid Yuferev & Vladimir Panchenko & Vadim Bolshev & Marek Jasiński & Aymen Flah, 2022. "Method for the Automated Inspection of the Surfaces of Photovoltaic Modules," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    3. Mireille B. Tadie Fogaing & Arman Hemmati & Carlos F. Lange & Brian A. Fleck, 2019. "Performance of Turbulence Models in Simulating Wind Loads on Photovoltaics Modules," Energies, MDPI, vol. 12(17), pages 1-16, August.
    4. Youngjin Choi & Yangrok Choi & Jun-sang Cho & Dongwoo Kim & Jungsik Kong, 2023. "Utilization and Verification of Imaging Technology in Smart Bridge Inspection System: An Application Study," Sustainability, MDPI, vol. 15(2), pages 1-31, January.
    5. Di Tommaso, Antonio & Betti, Alessandro & Fontanelli, Giacomo & Michelozzi, Benedetto, 2022. "A multi-stage model based on YOLOv3 for defect detection in PV panels based on IR and visible imaging by unmanned aerial vehicle," Renewable Energy, Elsevier, vol. 193(C), pages 941-962.
    6. Thiago B. Murari & Aloisio S. Nascimento Filho & Marcelo A. Moret & Sergio Pitombo & Alex A. B. Santos, 2020. "Self-Affine Analysis of ENSO in Solar Radiation," Energies, MDPI, vol. 13(18), pages 1-17, September.
    7. Şevik, Seyfi & Aktaş, Ahmet, 2022. "Performance enhancing and improvement studies in a 600 kW solar photovoltaic (PV) power plant; manual and natural cleaning, rainwater harvesting and the snow load removal on the PV arrays," Renewable Energy, Elsevier, vol. 181(C), pages 490-503.
    8. 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.
    9. Murat Altıntaş & Serdal Arslan, 2021. "The Study of Dust Removal Using Electrostatic Cleaning System for Solar Panels," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    10. Mahmoud Dhimish & Pavlos I. Lazaridis, 2022. "Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-16, November.

    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. 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.
    2. 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.
    3. Sunme Park & Soyeong Park & Myungsun Kim & Euiseok Hwang, 2020. "Clustering-Based Self-Imputation of Unlabeled Fault Data in a Fleet of Photovoltaic Generation Systems," Energies, MDPI, vol. 13(3), pages 1-16, February.
    4. Gorjian, Shiva & Bousi, Erion & Özdemir, Özal Emre & Trommsdorff, Max & Kumar, Nallapaneni Manoj & Anand, Abhishek & Kant, Karunesh & Chopra, Shauhrat S., 2022. "Progress and challenges of crop production and electricity generation in agrivoltaic systems using semi-transparent photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Thiago B. Murari & Aloisio S. Nascimento Filho & Marcelo A. Moret & Sergio Pitombo & Alex A. B. Santos, 2020. "Self-Affine Analysis of ENSO in Solar Radiation," Energies, MDPI, vol. 13(18), pages 1-17, September.
    6. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    7. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    8. Amro M Elshurafa & Abdel Rahman Muhsen, 2019. "The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    9. Carlos Toledo & Lucía Serrano-Lujan & Jose Abad & Antonio Lampitelli & Antonio Urbina, 2019. "Measurement of Thermal and Electrical Parameters in Photovoltaic Systems for Predictive and Cross-Correlated Monitorization," Energies, MDPI, vol. 12(4), pages 1-20, February.
    10. Joshua M. Pearce & Nelson Sommerfeldt, 2021. "Economics of Grid-Tied Solar Photovoltaic Systems Coupled to Heat Pumps: The Case of Northern Climates of the U.S. and Canada," Energies, MDPI, vol. 14(4), pages 1-17, February.
    11. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    12. 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.
    13. Azraff Bin Rozmi, Mohd Daniel & Thirunavukkarasu, Gokul Sidarth & Jamei, Elmira & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Stojcevski, Alex & Horan, Ben, 2019. "Role of immersive visualization tools in renewable energy system development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    14. Triki-Lahiani, Asma & Bennani-Ben Abdelghani, Afef & Slama-Belkhodja, Ilhem, 2018. "Fault detection and monitoring systems for photovoltaic installations: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2680-2692.
    15. Tingting Pei & Xiaohong Hao, 2019. "A Fault Detection Method for Photovoltaic Systems Based on Voltage and Current Observation and Evaluation," Energies, MDPI, vol. 12(9), pages 1-16, May.
    16. Chelsea Schelly & Don Lee & Elise Matz & Joshua M. Pearce, 2021. "Applying a Relationally and Socially Embedded Decision Framework to Solar Photovoltaic Adoption: A Conceptual Exploration," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    17. Giovanni Cipriani & Antonino D’Amico & Stefania Guarino & Donatella Manno & Marzia Traverso & Vincenzo Di Dio, 2020. "Convolutional Neural Network for Dust and Hotspot Classification in PV Modules," Energies, MDPI, vol. 13(23), pages 1-17, December.
    18. Mikkel Schou Nielsen & Ivan Nikolov & Emil Krog Kruse & Jørgen Garnæs & Claus Brøndgaard Madsen, 2020. "High-Resolution Structure-from-Motion for Quantitative Measurement of Leading-Edge Roughness," Energies, MDPI, vol. 13(15), pages 1-17, July.
    19. Julius Peter Landwehr & Niklas Kühl & Jannis Walk & Mario Gnädig, 2022. "Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 707-728, December.
    20. Li, Chenxi & Yang, Yongheng & Spataru, Sergiu & Zhang, Kanjian & Wei, Haikun, 2021. "A robust parametrization method of photovoltaic modules for enhancing one-diode model accuracy under varying operating conditions," Renewable Energy, Elsevier, vol. 168(C), pages 764-778.

    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:12:y:2019:i:5:p:795-:d:209547. 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.