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Underground MV Network Failures’ Waveform Characteristics—An Investigation

Author

Listed:
  • Miguel Louro

    (EDP Distribuição, 1050-044 Lisbon, Portugal)

  • Luís Ferreira

    (Department of Electrical Engineering and Computers, Instituto Superior Técnico, 1049-001 Lisbon, Portugal)

Abstract

The authors seek to investigate the characteristics of outage-causing faults that can be observed in a short time frame after their occurrence: waveform of the voltages and currents. The aim is to identify which characteristics can be used to estimate the failure type immediately after its occurrence. This paper lays the groundwork to determine which features display a stronger relation to four failure types with the aim of using this information in a later work, not presented in this paper, aimed at designing a reliable failure type estimator from readily available data. This paper focuses on the most common failures of the underground cable MV networks in Portugal: cable insulation; cable joint; secondary substation busbar; and excavation-motivated failures. A set of 206 waveform records of real underground MV network failures was available for analysis. After investigating the waveforms, the authors identified seven waveform characteristics which can be used for failure type estimation. Fault type characteristics can be used to distinguish secondary substation failures from the remaining failure types. Fault evolution does not yield relevant information. Fault self-extinction phenomenon was not observed in excavation-caused failures. There are differences for self-extinction characteristics between secondary substation busbar failures and the cable insulation and joint failures. Fault inception instant and arc voltage are two characteristics which are shown to have a promising merit to the identification process of failure types. Finally, fault intra-cycle repetitive extinction results have been found to be very similar for cable insulation failures and joint failures, but otherwise different regarding the remaining failure types.

Suggested Citation

  • Miguel Louro & Luís Ferreira, 2021. "Underground MV Network Failures’ Waveform Characteristics—An Investigation," Energies, MDPI, vol. 14(5), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1216-:d:504737
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    References listed on IDEAS

    as
    1. Hisahide Nakamura & Yukio Mizuno, 2020. "Method for Diagnosing a Short-Circuit Fault in the Stator Winding of a Motor Based on Parameter Identification of Features and a Support Vector Machine," Energies, MDPI, vol. 13(9), pages 1-15, May.
    2. Govind Sahay Yogee & Om Prakash Mahela & Kapil Dev Kansal & Baseem Khan & Rajendra Mahla & Hassan Haes Alhelou & Pierluigi Siano, 2020. "An Algorithm for Recognition of Fault Conditions in the Utility Grid with Renewable Energy Penetration," Energies, MDPI, vol. 13(9), pages 1-22, May.
    3. Ning Liu & Bo Fan & Xianyong Xiao & Xiaomei Yang, 2019. "Cable Incipient Fault Identification with a Sparse Autoencoder and a Deep Belief Network," Energies, MDPI, vol. 12(18), pages 1-15, September.
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    Citations

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    Cited by:

    1. Vitor Hugo Ferreira & André da Costa Pinho & Dickson Silva de Souza & Bárbara Siqueira Rodrigues, 2021. "A New Clustering Approach for Automatic Oscillographic Records Segmentation," Energies, MDPI, vol. 14(20), pages 1-18, October.
    2. Miguel Louro & Luís Ferreira, 2022. "Estimation of Underground MV Network Failure Types by Applying Machine Learning Methods to Indirect Observations," Energies, MDPI, vol. 15(17), pages 1-15, August.

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