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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
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    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.

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