IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7760-d948230.html
   My bibliography  Save this article

Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance

Author

Listed:
  • Andreas Livera

    (PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus)

  • Georgios Tziolis

    (PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus)

  • Jose G. Franquelo

    (Isotrol SA, Isaac Newton 3, 41092 Seville, Spain)

  • Ruben Gonzalez Bernal

    (Isotrol SA, Isaac Newton 3, 41092 Seville, Spain)

  • George E. Georghiou

    (PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering, University of Cyprus, Nicosia 1678, Cyprus)

Abstract

A cloud-based platform for reducing photovoltaic (PV) operation and maintenance (O&M) costs and improving lifetime performance is proposed in this paper. The platform incorporates a decision support system (DSS) engine and data-driven functionalities for data cleansing, PV system modeling, early fault diagnosis and provision of O&M recommendations. It can ensure optimum performance by monitoring in real time the operating state of PV assets, detecting faults at early stages and suggesting field mitigation actions based on energy loss analysis and incidents criticality evaluation. The developed platform was benchmarked using historical data from a test PV power plant installed in the Mediterranean region. The obtained results showed the effectiveness of the incorporated functionalities for data cleansing and system modeling as well as the platform’s capability for automated PV asset diagnosis and maintenance by providing recommendations for resolving the detected underperformance issues. Based on the DSS recommendations, approximately 7% of lost energy production could be recovered by performing field mitigation activities (e.g., corrective actions).

Suggested Citation

  • Andreas Livera & Georgios Tziolis & Jose G. Franquelo & Ruben Gonzalez Bernal & George E. Georghiou, 2022. "Cloud-Based Platform for Photovoltaic Assets Diagnosis and Maintenance," Energies, MDPI, vol. 15(20), pages 1-25, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7760-:d:948230
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7760/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7760/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mansouri, Majdi & Hajji, Mansour & Trabelsi, Mohamed & Harkat, Mohamed Faouzi & Al-khazraji, Ayman & Livera, Andreas & Nounou, Hazem & Nounou, Mohamed, 2018. "An effective statistical fault detection technique for grid connected photovoltaic systems based on an improved generalized likelihood ratio test," Energy, Elsevier, vol. 159(C), pages 842-856.
    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. 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).

    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. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.
    2. 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.
    3. Bakdi, Azzeddine & Bounoua, Wahiba & Mekhilef, Saad & Halabi, Laith M., 2019. "Nonparametric Kullback-divergence-PCA for intelligent mismatch detection and power quality monitoring in grid-connected rooftop PV," Energy, Elsevier, vol. 189(C).
    4. Khadija Attouri & Majdi Mansouri & Mansour Hajji & Abdelmalek Kouadri & Kais Bouzrara & Hazem Nounou, 2023. "Wind Power Converter Fault Diagnosis Using Reduced Kernel PCA-Based BiLSTM," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    5. Fezai, R. & Mansouri, M. & Trabelsi, M. & Hajji, M. & Nounou, H. & Nounou, M., 2019. "Online reduced kernel GLRT technique for improved fault detection in photovoltaic systems," Energy, Elsevier, vol. 179(C), pages 1133-1154.
    6. 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.
    7. Rouani, Lahcene & Harkat, Mohamed Faouzi & Kouadri, Abdelmalek & Mekhilef, Saad, 2021. "Shading fault detection in a grid-connected PV system using vertices principal component analysis," Renewable Energy, Elsevier, vol. 164(C), pages 1527-1539.
    8. Tabar, Vahid Sohrabi & Ghassemzadeh, Saeid & Tohidi, Sajjad, 2021. "Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid," Energy, Elsevier, vol. 220(C).
    9. Manel Marweni & Mansour Hajji & Majdi Mansouri & Mohamed Fouazi Mimouni, 2023. "Photovoltaic Power Forecasting Using Multiscale-Model-Based Machine Learning Techniques," Energies, MDPI, vol. 16(12), pages 1-16, June.

    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:15:y:2022:i:20:p:7760-:d:948230. 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.