IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i5p1261-d505661.html
   My bibliography  Save this article

A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic

Author

Listed:
  • Christopher Gradwohl

    (Energy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, Austria)

  • Vesna Dimitrievska

    (SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria)

  • Federico Pittino

    (SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria)

  • Wolfgang Muehleisen

    (SAL Silicon Austria Labs GmbH, Europastr.12, 9524 Villach, Austria)

  • András Montvay

    (SAL Silicon Austria Labs GmbH, Inffeldgasse 33, 8010 Graz, Austria)

  • Franz Langmayr

    (Uptime Engineering GmbH, Schoenaugasse 7/2, 8010 Graz, Austria)

  • Thomas Kienberger

    (Energy Network Technology, Montanuniversitaet Leoben, Franz-Josef Str18, 8700 Leoben, Austria)

Abstract

Photovoltaic (PV) technology allows large-scale investments in a renewable power-generating system at a competitive levelized cost of electricity (LCOE) and with a low environmental impact. Large-scale PV installations operate in a highly competitive market environment where even small performance losses have a high impact on profit margins. Therefore, operation at maximum performance is the key for long-term profitability. This can be achieved by advanced performance monitoring and instant or gradual failure detection methodologies. We present in this paper a combined approach on model-based fault detection by means of physical and statistical models and failure diagnosis based on physics of failure. Both approaches contribute to optimized PV plant operation and maintenance based on typically available supervisory control and data acquisition (SCADA) data. The failure detection and diagnosis capabilities were demonstrated in a case study based on six years of SCADA data from a PV plant in Slovenia. In this case study, underperforming values of the inverters of the PV plant were reliably detected and possible root causes were identified. Our work has led us to conclude that the combined approach can contribute to an efficient and long-term operation of photovoltaic power plants with a maximum energy yield and can be applied to the monitoring of photovoltaic plants.

Suggested Citation

  • Christopher Gradwohl & Vesna Dimitrievska & Federico Pittino & Wolfgang Muehleisen & András Montvay & Franz Langmayr & Thomas Kienberger, 2021. "A Combined Approach for Model-Based PV Power Plant Failure Detection and Diagnostic," Energies, MDPI, vol. 14(5), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1261-:d:505661
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/5/1261/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/5/1261/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. A. Sayed & M. El-Shimy & M. El-Metwally & M. Elshahed, 2019. "Reliability, Availability and Maintainability Analysis for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 12(7), pages 1-18, March.
    2. Alexander Frick & George Makrides & Markus Schubert & Matthias Schlecht & George E. Georghiou, 2020. "Degradation Rate Location Dependency of Photovoltaic Systems," Energies, MDPI, vol. 13(24), pages 1-20, December.
    3. 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.
    4. Mühleisen, W. & Hirschl, C. & Brantegger, G. & Neumaier, L. & Spielberger, M. & Sonnleitner, H. & Kubicek, B. & Ujvari, G. & Ebner, R. & Schwark, M. & Eder, G.C. & Voronko, Y. & Knöbl, K. & Stoicescu,, 2019. "Scientific and economic comparison of outdoor characterisation methods for photovoltaic power plants," Renewable Energy, Elsevier, vol. 134(C), pages 321-329.
    5. 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.
    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. Ding, Liping & Zhang, Zumeng & Dai, Qiyao & Zhu, Yuxuan & Shi, Yin, 2023. "Alternative operational modes for Chinese PV poverty alleviation power stations: Economic impacts on stakeholders," Utilities Policy, Elsevier, vol. 82(C).
    2. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    3. Joong-Woo Shin & Kwang-Hoon Yoon & Hui-Seok Chai & Jae-Chul Kim, 2022. "Reliability-Centered Maintenance Scheduling of Photovoltaic Components According to Failure Effects," Energies, MDPI, vol. 15(7), pages 1-15, March.
    4. Fausto Pedro García Márquez, 2022. "Maintenance Management in Solar Energy Systems," Energies, MDPI, vol. 15(10), pages 1-3, May.
    5. 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. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    2. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    3. Turgut Karahüseyin & Serkan Abbasoğlu, 2022. "Performance Loss Rates of a 1 MWp PV Plant with Various Tilt Angle, Orientation and Installed Environment in the Capital of Cyprus," Sustainability, MDPI, vol. 14(15), pages 1-23, July.
    4. Oyeniyi A. Alimi & Edson L. Meyer & Olufemi I. Olayiwola, 2022. "Solar Photovoltaic Modules’ Performance Reliability and Degradation Analysis—A Review," Energies, MDPI, vol. 15(16), pages 1-28, August.
    5. 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.
    6. 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.
    7. Adel Mellit & Omar Herrak & Catalina Rus Casas & Alessandro Massi Pavan, 2021. "A Machine Learning and Internet of Things-Based Online Fault Diagnosis Method for Photovoltaic Arrays," Sustainability, MDPI, vol. 13(23), pages 1-14, November.
    8. Veljanovski, N. & ÄŒepin, M., 2024. "Event tree-based risk and financial assessment for power plants," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    9. Bouaichi, Abdellatif & El Amrani, Aumeur & Ouhadou, Malika & Lfakir, Aberrazak & Messaoudi, Choukri, 2020. "In-situ performance and degradation of three different photovoltaic module technologies installed in arid climate of Morocco," Energy, Elsevier, vol. 190(C).
    10. Bilal Taghezouit & Fouzi Harrou & Cherif Larbes & Ying Sun & Smail Semaoui & Amar Hadj Arab & Salim Bouchakour, 2022. "Intelligent Monitoring of Photovoltaic Systems via Simplicial Empirical Models and Performance Loss Rate Evaluation under LabVIEW: A Case Study," Energies, MDPI, vol. 15(21), pages 1-30, October.
    11. Joong-Woo Shin & Kwang-Hoon Yoon & Hui-Seok Chai & Jae-Chul Kim, 2022. "Reliability-Centered Maintenance Scheduling of Photovoltaic Components According to Failure Effects," Energies, MDPI, vol. 15(7), pages 1-15, March.
    12. Hak-Ju Lee & Byeong-Chan Oh & Seok-Woong Kim & Sung-Yul Kim, 2020. "V2G Strategy for Improvement of Distribution Network Reliability Considering Time Space Network of EVs," Energies, MDPI, vol. 13(17), pages 1-19, August.
    13. Mik, Krzysztof & Bugaj, Marcin & Chaja, Patryk, 2021. "The evaluation of the snail track affected photovoltaic modules by different methods after 3-year operating in central Poland," Renewable Energy, Elsevier, vol. 163(C), pages 504-516.
    14. Cristina Morel & Ahmad Akrad & Rabia Sehab & Toufik Azib & Cherif Larouci, 2022. "Open-Circuit Fault-Tolerant Strategy for Interleaved Boost Converters via Filippov Method," Energies, MDPI, vol. 15(1), pages 1-23, January.
    15. Waqar Akram, M. & Li, Guiqiang & Jin, Yi & Chen, Xiao, 2022. "Failures of Photovoltaic modules and their Detection: A Review," Applied Energy, Elsevier, vol. 313(C).
    16. Agnieszka Żelazna & Justyna Gołębiowska & Agata Zdyb & Artur Pawłowski, 2020. "A Hybrid vs. On-Grid Photovoltaic System: Multicriteria Analysis of Environmental, Economic, and Technical Aspects in Life Cycle Perspective," Energies, MDPI, vol. 13(15), pages 1-16, August.
    17. Álvaro Rodríguez-Martinez & Carlos Rodríguez-Monroy, 2021. "Economic Analysis and Modelling of Rooftop Photovoltaic Systems in Spain for Industrial Self-Consumption," Energies, MDPI, vol. 14(21), pages 1-32, November.
    18. Pramod R. Sonawane & Sheetal Bhandari & Rajkumar Bhimgonda Patil & Sameer Al-Dahidi, 2023. "Reliability and Criticality Analysis of a Large-Scale Solar Photovoltaic System Using Fault Tree Analysis Approach," Sustainability, MDPI, vol. 15(5), pages 1-24, March.
    19. Romero-Fiances, Irene & Livera, Andreas & Theristis, Marios & Makrides, George & Stein, Joshua S. & Nofuentes, Gustavo & de la Casa, Juan & Georghiou, George E., 2022. "Impact of duration and missing data on the long-term photovoltaic degradation rate estimation," Renewable Energy, Elsevier, vol. 181(C), pages 738-748.
    20. Michael Felix Pacevicius & Marilia Ramos & Davide Roverso & Christian Thun Eriksen & Nicola Paltrinieri, 2022. "Managing Heterogeneous Datasets for Dynamic Risk Analysis of Large-Scale Infrastructures," Energies, MDPI, vol. 15(9), pages 1-40, April.

    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:5:p:1261-:d:505661. 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.