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A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations

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  • Giovanni Betta

    (Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy)

  • Domenico Capriglione

    (Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy)

  • Luigi Ferrigno

    (Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy)

  • Marco Laracca

    (Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy)

  • Gianfranco Miele

    (Department of Electrical and Information Engineering, University of Cassino and Southern Lazio, 03043 Cassino, Italy)

  • Nello Polese

    (Faculty of Economics, Mercatorum University, 00186 Rome, Italy)

  • Silvia Sangiovanni

    (Department of Astronautics, Electrical and Energy Engineering, Sapienza University of Rome, 00184 Rome, Italy)

Abstract

The reliability of systems and components is a fundamental need for the efficient development of a smart distribution grid. In fact, the presence of a fault in one component of the grid could potentially lead to a service interruption and loss of profit. Since faults cannot be avoided, the introduction of a diagnostic scheme could predict the fault of a component in order to carry out predictive maintenance. In this framework, this paper proposes a novel Fault Detection and Isolation (FDI) scheme for AC/DC converters in MV/LV substations. In order to improve the reliability of the FDI procedure, the system architecture includes also an Instrument Fault Detection and Isolation section for identifying faults that could occur on the instruments and sensors involved in the monitoring process of the AC/DC converter. The proposed architecture is scalable, easily upgradable, and uses cost-effective sensors. Tests, carried out on a real test site, have demonstrated the efficacy of the proposal showing very good IFDI diagnostic performance for the 12 types of faults tested. Furthermore, as the FDI diagnostic performance regards, it shows a detection rate close to 100%.

Suggested Citation

  • Giovanni Betta & Domenico Capriglione & Luigi Ferrigno & Marco Laracca & Gianfranco Miele & Nello Polese & Silvia Sangiovanni, 2021. "A Fault Diagnostic Scheme for Predictive Maintenance of AC/DC Converters in MV/LV Substations," Energies, MDPI, vol. 14(22), pages 1-23, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7668-:d:680437
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    References listed on IDEAS

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    1. Fábio Vinicius Vieira Bezerra & Gervásio Protásio Santos Cavalcante & Fabrício Jose Brito Barros & Maria Emília Lima Tostes & Ubiratan Holanda Bezerra, 2020. "Methodology for Predictive Assessment of Failures in Power Station Electric Bays Using the Load Current Frequency Spectrum," Energies, MDPI, vol. 13(19), pages 1-14, October.
    2. Jing Wu & Kun Li & Jing Sun & Li Xie, 2018. "A Novel Integrated Method to Diagnose Faults in Power Transformers," Energies, MDPI, vol. 11(11), pages 1-8, November.
    3. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
    4. Hare, James & Shi, Xiaofang & Gupta, Shalabh & Bazzi, Ali, 2016. "Fault diagnostics in smart micro-grids: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1114-1124.
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