IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7278-d671487.html
   My bibliography  Save this article

Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA

Author

Listed:
  • Tito G. Amaral

    (SustainRD, EST Setubal, Polytechnic Institute of Setúbal, 2914-508 Setúbal, Portugal)

  • Vitor Fernão Pires

    (SustainRD, EST Setubal, Polytechnic Institute of Setúbal, 2914-508 Setúbal, Portugal
    INESC-ID, 1000-029 Lisboa, Portugal)

  • Armando J. Pires

    (SustainRD, EST Setubal, Polytechnic Institute of Setúbal, 2914-508 Setúbal, Portugal
    CTS-UNINOVA, 2829-516 Costa da Caparica, Portugal)

Abstract

Photovoltaic power plants nowadays play an important role in the context of energy generation based on renewable sources. With the purpose of obtaining maximum efficiency, the PV modules of these power plants are installed in trackers. However, the mobile structure of the trackers is subject to faults, which can compromise the desired perpendicular position between the PV modules and the brightest point in the sky. So, the diagnosis of a fault in the trackers is fundamental to ensure the maximum energy production. Approaches based on sensors and statistical methods have been researched but they are expensive and time consuming. To overcome these problems, a new method is proposed for the fault diagnosis in the trackers of the PV systems based on a machine learning approach. In this type of approach the developed method can be classified into two major categories: supervised and unsupervised. In accordance with this, to implement the desired fault diagnosis, an unsupervised method based on a new image processing algorithm to determine the PV slopes is proposed. The fault detection is obtained comparing the slopes of several modules. This algorithm is based on a new image processing approach in which principal component analysis (PCA) is used. Instead of using the PCA to reduce the data dimension, as is usual, it is proposed to use it to determine the slope of an object. The use of the proposed approach presents several benefits, namely, avoiding the use of a wide range of data and specific sensors, fast detection and reliability even with incomplete images due to reflections and other problems. Based on this algorithm, a deviation index is also proposed that will be used to discriminate the panel(s) under fault. Several test cases are used to test and validate the proposed approach. From the obtained results, it is possible to verify that the PCA can successfully be adapted and used in image processing algorithms to determine the slope of the PV modules and so effectively detect a fault in the tracker, even when there are incomplete parts of an object in the image.

Suggested Citation

  • Tito G. Amaral & Vitor Fernão Pires & Armando J. Pires, 2021. "Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA," Energies, MDPI, vol. 14(21), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7278-:d:671487
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7278/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7278/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sidek, M.H.M. & Azis, N. & Hasan, W.Z.W. & Ab Kadir, M.Z.A. & Shafie, S. & Radzi, M.A.M., 2017. "Automated positioning dual-axis solar tracking system with precision elevation and azimuth angle control," Energy, Elsevier, vol. 124(C), pages 160-170.
    2. Eleonora Arena & Alessandro Corsini & Roberto Ferulano & Dario Alfio Iuvara & Eric Stefan Miele & Lorenzo Ricciardi Celsi & Nour Alhuda Sulieman & Massimo Villari, 2021. "Anomaly Detection in Photovoltaic Production Factories via Monte Carlo Pre-Processed Principal Component Analysis," Energies, MDPI, vol. 14(13), pages 1-16, July.
    3. Papadakis, Kostas & Koutroulis, Eftichios & Kalaitzakis, Kostas, 2005. "A server database system for remote monitoring and operational evaluation of renewable energy sources plants," Renewable Energy, Elsevier, vol. 30(11), pages 1649-1669.
    4. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model," Energies, MDPI, vol. 12(7), pages 1-15, March.
    5. Tahmina Khatun, 2009. "Measuring environmental degradation by using principal component analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 11(2), pages 439-457, April.
    6. Hamid Iftikhar & Eduardo Sarquis & P. J. Costa Branco, 2021. "Why Can Simple Operation and Maintenance (O&M) Practices in Large-Scale Grid-Connected PV Power Plants Play a Key Role in Improving Its Energy Output?," Energies, MDPI, vol. 14(13), pages 1-29, June.
    7. Venkateswari, R. & Sreejith, S., 2019. "Factors influencing the efficiency of photovoltaic system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 376-394.
    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. Robert Ulewicz & Dominika Siwiec & Andrzej Pacana, 2023. "A New Model of Pro-Quality Decision Making in Terms of Products’ Improvement Considering Customer Requirements," Energies, MDPI, vol. 16(11), pages 1-22, May.
    3. Andrzej Pacana & Dominika Siwiec, 2022. "Model to Predict Quality of Photovoltaic Panels Considering Customers’ Expectations," Energies, MDPI, vol. 15(3), pages 1-33, February.
    4. Tadeusz Olejarz & Dominika Siwiec & Andrzej Pacana, 2022. "Method of Qualitative–Environmental Choice of Devices Converting Green Energy," Energies, MDPI, vol. 15(23), pages 1-22, November.
    5. Yin Chen & Zhenli Tang & Xiaofeng Weng & Min He & Sheng Zhou & Ziqiang Liu & Tao Jin, 2024. "A Diagnostic Method for Open-Circuit Faults in DC Charging Stations Based on Improved S-Transform and LightGBM," Energies, MDPI, vol. 17(2), pages 1-26, January.
    6. Benamar Bouyeddou & Fouzi Harrou & Bilal Taghezouit & Ying Sun & Amar Hadj Arab, 2022. "Improved Semi-Supervised Data-Mining-Based Schemes for Fault Detection in a Grid-Connected Photovoltaic System," Energies, MDPI, vol. 15(21), pages 1-22, October.
    7. Grzegorz Ostasz & Dominika Siwiec & Andrzej Pacana, 2022. "Model to Determine the Best Modifications of Products with Consideration Customers’ Expectations," Energies, MDPI, vol. 15(21), pages 1-21, October.
    8. Zhao, Xiaolong & Song, Chonghui & Zhang, Haifeng & Sun, Xianrui & Zhao, Jing, 2023. "HRNet-based automatic identification of photovoltaic module defects using electroluminescence images," Energy, Elsevier, vol. 267(C).
    9. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, 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. Vivar, M. & H, Sharon & Fuentes, M., 2024. "Photovoltaic system adoption in water related technologies – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    3. Rosanna Salvia & Andrea Colantoni & Leonardo Bianchini & Gianluca Egidi & Gloria Polinesi & Luca Salvati & Giovanni Quaranta, 2022. "‘Old’ Territorial Disparities and ‘New’ Spatial Patterns: Unraveling the Latent Nexus between Sustainable Development and Desertification Risk in Italy," Economies, MDPI, vol. 10(2), pages 1-14, February.
    4. Goharian, Ali & Daneshjoo, Khosro & Shaeri, Jalil & Mahdavinejad, Mohammadjavad & Yeganeh, Mansour, 2023. "A designerly approach to daylight efficiency of central light-well; combining manual with NSGA-II algorithm optimization," Energy, Elsevier, vol. 276(C).
    5. Barbón, A. & Fernández-Rubiera, J.A. & Martínez-Valledor, L. & Pérez-Fernández, A. & Bayón, L., 2021. "Design and construction of a solar tracking system for small-scale linear Fresnel reflector with three movements," Applied Energy, Elsevier, vol. 285(C).
    6. Mehdi, Maryam & Ammari, Nabil & Alami Merrouni, Ahmed & El Gallassi, Hicham & Dahmani, Mohamed & Ghennioui, Abdellatif, 2023. "An experimental comparative analysis of different PV technologies performance including the influence of hot-arid climatic parameters: Toward a realistic yield assessment for desert locations," Renewable Energy, Elsevier, vol. 205(C), pages 695-716.
    7. Abdulla, Hind & Sleptchenko, Andrei & Nayfeh, Ammar, 2024. "Photovoltaic systems operation and maintenance: A review and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 195(C).
    8. Mrittika Basu & Satoshi Hoshino & Shizuka Hashimoto, 2016. "A pragmatic analysis of water supply and demand, and adaptive capacity in rural areas: development of Rural Water Insecurity Index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 447-466, March.
    9. Pirayawaraporn, Alongkorn & Sappaniran, Sahapol & Nooraksa, Sarawin & Prommai, Chanon & Chindakham, Nachaya & Jamroen, Chaowanan, 2023. "Innovative sensorless dual-axis solar tracking system using particle filter," Applied Energy, Elsevier, vol. 338(C).
    10. Victor Arturo Martinez Lopez & Ugnė Žindžiūtė & Hesan Ziar & Miro Zeman & Olindo Isabella, 2022. "Study on the Effect of Irradiance Variability on the Efficiency of the Perturb-and-Observe Maximum Power Point Tracking Algorithm," Energies, MDPI, vol. 15(20), pages 1-12, October.
    11. Dragos Machidon & Marcel Istrate, 2023. "Tilt Angle Adjustment for Incident Solar Energy Increase: A Case Study for Europe," Sustainability, MDPI, vol. 15(8), pages 1-12, April.
    12. Carlos Morón & Daniel Ferrández & Pablo Saiz & Gabriela Vega & Jorge Pablo Díaz, 2017. "New Prototype of Photovoltaic Solar Tracker Based on Arduino," Energies, MDPI, vol. 10(9), pages 1-13, August.
    13. Ossai, Chinedu I. & Boswell, Brian & Davies, Ian J., 2014. "Sustainable asset integrity management: Strategic imperatives for economic renewable energy generation," Renewable Energy, Elsevier, vol. 67(C), pages 143-152.
    14. Ruijin Zhu & Bo Tang & Wenhai Wei, 2022. "Ensemble Learning-Based Reactive Power Optimization for Distribution Networks," Energies, MDPI, vol. 15(6), pages 1-15, March.
    15. Srinivasan Vadivel & C. S. Boopthi & Sridhar Ramasamy & Mominul Ahsan & Julfikar Haider & Eduardo M. G. Rodrigues, 2021. "Performance Enhancement of a Partially Shaded Photovoltaic Array by Optimal Reconfiguration and Current Injection Schemes," Energies, MDPI, vol. 14(19), pages 1-21, October.
    16. Hameedullah Zaheb & Habibullah Amiry & Mikaeel Ahmadi & Habibullah Fedayi & Sajida Amiry & Atsushi Yona, 2023. "Maximizing Annual Energy Yield in a Grid-Connected PV Solar Power Plant: Analysis of Seasonal Tilt Angle and Solar Tracking Strategies," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    17. Seme, Sebastijan & Srpčič, Gregor & Kavšek, Domen & Božičnik, Stane & Letnik, Tomislav & Praunseis, Zdravko & Štumberger, Bojan & Hadžiselimović, Miralem, 2017. "Dual-axis photovoltaic tracking system – Design and experimental investigation," Energy, Elsevier, vol. 139(C), pages 1267-1274.
    18. Sergio Isai Palomino-Resendiz & Norma Beatriz Lozada-Castillo & Diego Alonso Flores-Hernández & Oscar Octavio Gutiérrez-Frías & Alberto Luviano-Juárez, 2021. "Adaptive Active Disturbance Rejection Control of Solar Tracking Systems with Partially Known Model," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    19. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    20. Gianfranco Di Lorenzo & Erika Stracqualursi & Leonardo Micheli & Salvatore Celozzi & Rodolfo Araneo, 2022. "Prognostic Methods for Photovoltaic Systems’ Underperformance and Degradation: Status, Perspectives, and Challenges," Energies, MDPI, vol. 15(17), pages 1-6, September.

    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:21:p:7278-:d:671487. 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.