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Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation

Author

Listed:
  • Tolga Yalçin

    (Power Electronics Department, Catalonia Institut for Energy Research—IREC, Jardins de les Dones de Negre 1, 2 a pl., Sant Adrià del Besòs, 08930 Barcelona, Spain)

  • Pol Paradell Solà

    (Power Electronics Department, Catalonia Institut for Energy Research—IREC, Jardins de les Dones de Negre 1, 2 a pl., Sant Adrià del Besòs, 08930 Barcelona, Spain)

  • Paschalia Stefanidou-Voziki

    (E.ON Digital Technology GmbH, Georg-Brauchle-Ring 52-54, 80992 Munich, Germany)

  • Jose Luis Domínguez-García

    (Power Electronics Department, Catalonia Institut for Energy Research—IREC, Jardins de les Dones de Negre 1, 2 a pl., Sant Adrià del Besòs, 08930 Barcelona, Spain)

  • Tugce Demirdelen

    (Departmentof Electrical and Electronics Engineering, Alparslan Turkes Science and Technology University—ATU, Balcalı Mah., South Campus 10 Street, No:1U, P.O. Box GP 561 Adana, Turkey)

Abstract

The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions.

Suggested Citation

  • Tolga Yalçin & Pol Paradell Solà & Paschalia Stefanidou-Voziki & Jose Luis Domínguez-García & Tugce Demirdelen, 2023. "Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation," Energies, MDPI, vol. 16(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5044-:d:1182667
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    References listed on IDEAS

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    1. Magnus Værbak & Joy Dalmacio Billanes & Bo Nørregaard Jørgensen & Zheng Ma, 2024. "A Digital Twin Framework for Simulating Distributed Energy Resources in Distribution Grids," Energies, MDPI, vol. 17(11), pages 1-36, May.
    2. Dorotea Dimitrova Angelova & Diego Carmona Fernández & Manuel Calderón Godoy & Juan Antonio Álvarez Moreno & Juan Félix González González, 2024. "A Review on Digital Twins and Its Application in the Modeling of Photovoltaic Installations," Energies, MDPI, vol. 17(5), pages 1-29, March.
    3. Xiaotong Dong & Jing Huang & Ningzhao Luo & Wenshan Hu & Zhongcheng Lei, 2023. "Design and Implementation of Digital Twin Diesel Generator Systems," Energies, MDPI, vol. 16(18), pages 1-16, September.
    4. 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).

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