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A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

Author

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  • Juan R. Lopez

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Jose de Jesus Camacho

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Pedro Ponce

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

  • Brian MacCleery

    (National Instruments Corporation, Austin, TX 78759, USA)

  • Arturo Molina

    (Tecnologico de Monterrey, School of Engineering and Sciences, Puente 222, Tlalpan, Mexico City 14380, Mexico)

Abstract

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification.

Suggested Citation

  • Juan R. Lopez & Jose de Jesus Camacho & Pedro Ponce & Brian MacCleery & Arturo Molina, 2022. "A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network," Energies, MDPI, vol. 15(19), pages 1-25, October.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7306-:d:933592
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    References listed on IDEAS

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    1. Luis Ibarra & Antonio Rosales & Pedro Ponce & Arturo Molina & Raja Ayyanar, 2017. "Overview of Real-Time Simulation as a Supporting Effort to Smart-Grid Attainment," Energies, MDPI, vol. 10(6), pages 1-24, June.
    2. Juan Roberto Lopez & Luis Ibarra & Pedro Ponce & Arturo Molina, 2021. "A Decentralized Passive Islanding Detection Method Based on the Variations of Estimated Droop Characteristics," Energies, MDPI, vol. 14(22), pages 1-19, November.
    3. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    4. Issam A. Smadi & Saher Albatran & Hamzeh J. Ahmad, 2018. "On the Performance Optimization of Two-Level Three-Phase Grid-Feeding Voltage-Source Inverters," Energies, MDPI, vol. 11(2), pages 1-17, February.
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    Cited by:

    1. Eduardo Gómez-Luna & John E. Candelo-Becerra & Juan C. Vasquez, 2023. "A New Digital Twins-Based Overcurrent Protection Scheme for Distributed Energy Resources Integrated Distribution Networks," Energies, MDPI, vol. 16(14), pages 1-23, July.
    2. do Amaral, J.V.S. & dos Santos, C.H. & Montevechi, J.A.B. & de Queiroz, A.R., 2023. "Energy Digital Twin applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

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