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Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes

Author

Listed:
  • Fabrizio Bonacina

    (Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Alessandro Corsini

    (Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Lucio Cardillo

    (SED Solutions, 03013 Ferentino, Italy)

  • Francesca Lucchetta

    (Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

This paper presents a novel data-driven approach, based on sensor network analysis in Photovoltaic (PV) power plants, to unveil hidden precursors in failure modes. The method is based on the analysis of signals from PV plant monitoring, and advocates the use of graph modeling techniques to reconstruct and investigate the connectivity among PV field sensors, as is customary for Complex Network Analysis (CNA) approaches. Five month operation data are used in the present study. The results showed that the proposed methodology is able to discover specific hidden dynamics, also referred to as emerging properties in a Complexity Science perspective, which are not visible in the observation of individual sensor signal but are closely linked to the relationships occurring at the system level. The application of exploratory data analysis techniques on those properties demonstrated, for the specific plant under scrutiny, potential for early fault detection.

Suggested Citation

  • Fabrizio Bonacina & Alessandro Corsini & Lucio Cardillo & Francesca Lucchetta, 2019. "Complex Network Analysis of Photovoltaic Plant Operations and Failure Modes," Energies, MDPI, vol. 12(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1995-:d:234074
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    References listed on IDEAS

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    1. Mellit, A. & Tina, G.M. & Kalogirou, S.A., 2018. "Fault detection and diagnosis methods for photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1-17.
    2. Antonelli, Marco & Desideri, Umberto, 2014. "The doping effect of Italian feed-in tariffs on the PV market," Energy Policy, Elsevier, vol. 67(C), pages 583-594.
    3. Carbone, Anna & Jensen, Meiko & Sato, Aki-Hiro, 2016. "Challenges in data science: a complex systems perspective," Chaos, Solitons & Fractals, Elsevier, vol. 90(C), pages 1-7.
    4. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    5. 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.
    6. Ferdinando Chiacchio & Fabio Famoso & Diego D’Urso & Sebastian Brusca & Jose Ignacio Aizpurua & Luca Cedola, 2018. "Dynamic Performance Evaluation of Photovoltaic Power Plant by Stochastic Hybrid Fault Tree Automaton Model," Energies, MDPI, vol. 11(2), pages 1-22, January.
    7. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    8. Lacal Arantegui, Roberto & Jäger-Waldau, Arnulf, 2018. "Photovoltaics and wind status in the European Union after the Paris Agreement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2460-2471.
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    2. Sara Antomarioni & Marjorie Maria Bellinello & Maurizio Bevilacqua & Filippo Emanuele Ciarapica & Renan Favarão da Silva & Gilberto Francisco Martha de Souza, 2020. "A Data-Driven Approach to Extend Failure Analysis: A Framework Development and a Case Study on a Hydroelectric Power Plant," Energies, MDPI, vol. 13(23), pages 1-16, December.
    3. Piotr Hadaj & Dominik Strzałka, 2020. "Modelling Selected Parameters of Power Grid Network in the South-Eastern Part of Poland: The Case Study," Energies, MDPI, vol. 13(1), pages 1-17, January.
    4. Lin, Wenye & Ma, Zhenjun & Li, Kehua & Tyagi, V.V. & Pandey, A.K., 2021. "A dynamic simulation platform for fault modelling and characterisation of building integrated photovoltaics," Renewable Energy, Elsevier, vol. 179(C), pages 963-981.

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