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Failure Prevention and Malfunction Localization in Underground Medium Voltage Cables

Author

Listed:
  • Igor Aizenberg

    (Department of Computer Science, Manhattan College, Riverdale, New York, NY 10471, USA)

  • Riccardo Belardi

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

  • Marco Bindi

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

  • Francesco Grasso

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

  • Stefano Manetti

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

  • Antonio Luchetta

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

  • Maria Cristina Piccirilli

    (Department of Information Engineering, School of Engineering, University of Florence, 50139 Firenze, Italy)

Abstract

A smart monitoring system capable of detecting and classifying the health conditions of MV (Medium Voltage) underground cables is presented in this work. Using the analysis technique proposed here, it is possible to prevent the occurrence of catastrophic failures in medium voltage underground lines, for which it is generally difficult to realize maintenance operations and carry out punctual inspections. This prognostic method is based on Frequency Response Analysis (FRA) and can be used online during normal network operation, resulting in a minimally invasive tool. In order to obtain the good results shown in the simulation section, it is necessary to develop a lamped equivalent circuit of the network branch under consideration. The standard π-model is used in this paper to analyse sections of a medium voltage cable and the parameter variations with temperature are used to classify the state of health of the line. In fact, the variation of the electrical parameters produces a corresponding variation in the frequency response. The proposed system is based on the use of a complex neural network with feedforward architecture. It processes the frequency response, allowing the classification of the cable conditions with an accuracy higher than 90%.

Suggested Citation

  • Igor Aizenberg & Riccardo Belardi & Marco Bindi & Francesco Grasso & Stefano Manetti & Antonio Luchetta & Maria Cristina Piccirilli, 2020. "Failure Prevention and Malfunction Localization in Underground Medium Voltage Cables," Energies, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:85-:d:468530
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