IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i17p6298-d900621.html
   My bibliography  Save this article

Estimation of Underground MV Network Failure Types by Applying Machine Learning Methods to Indirect Observations

Author

Listed:
  • Miguel Louro

    (E-REDES, 1050-044 Lisbon, Portugal)

  • Luís Ferreira

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

Abstract

Electrical utilities performance is measured by various indicators, of which the most important are very dependent on the interruption time after a failure in the network has occurred, such as SAIDI. Therefore, they are constantly looking for new techniques to decrease the fault location and repair times. A possibility to innovate in this field is to estimate the failed network component when a fault occurs. This paper presents the conclusion of an analysis carried out by the authors with the aim to estimate failure types of underground MV networks based on observable indirect variables. The variables needed to carry out the analysis must be available shortly after the failure occurrence, which is facilitated by a smart-grid infrastructure, to allow for a quick estimation. This paper uses the groundwork already carried out by the authors on ambient variables, historical variables, and disturbance recordings to design an estimator to predict between four MV cable network failure types. The paper presents relevant analyses on the design and performance of various machine learning classification algorithms for estimation of the types of MV cable network failures using real-world data. Optimization of performance was carried out, resulting in an estimator with an overall 68% accuracy rate. Accuracy rates of 94% for cable failure, 63% for excavations, and 79% secondary busbar failures were achieved; as for cable joints, the accuracy was poor due to the difficulty to identify a feature that can be used to separate this failure type from cable failures. Future work to improve that accuracy is discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6298-:d:900621
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/17/6298/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/17/6298/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Federico Gargiulo & Annalisa Liccardo & Rosario Schiano Lo Moriello, 2022. "A Non-Invasive Method Based on AI and Current Measurements for the Detection of Faults in Three-Phase Motors," Energies, MDPI, vol. 15(12), pages 1-19, June.
    2. Yiyi Zhang & Yuxuan Wang & Xianhao Fan & Wei Zhang & Ran Zhuo & Jian Hao & Zhen Shi, 2020. "An Integrated Model for Transformer Fault Diagnosis to Improve Sample Classification near Decision Boundary of Support Vector Machine," Energies, MDPI, vol. 13(24), pages 1-15, December.
    3. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
    4. Adam Bondyra & Marek Kołodziejczak & Radosław Kulikowski & Wojciech Giernacki, 2022. "An Acoustic Fault Detection and Isolation System for Multirotor UAV," Energies, MDPI, vol. 15(11), pages 1-19, May.
    5. Miguel Louro & Luís Ferreira, 2021. "Underground MV Network Failures’ Waveform Characteristics—An Investigation," Energies, MDPI, vol. 14(5), pages 1-14, February.
    6. Shujie Yang & Peikun Yang & Hao Yu & Jing Bai & Wuwei Feng & Yuxiang Su & Yulin Si, 2022. "A 2DCNN-RF Model for Offshore Wind Turbine High-Speed Bearing-Fault Diagnosis under Noisy Environment," Energies, MDPI, vol. 15(9), pages 1-16, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Josue A. Reyes-Malanche & Francisco J. Villalobos-Pina & Efraın Ramırez-Velasco & Eduardo Cabal-Yepez & Geovanni Hernandez-Gomez & Misael Lopez-Ramirez, 2023. "Short-Circuit Fault Diagnosis on Induction Motors through Electric Current Phasor Analysis and Fuzzy Logic," Energies, MDPI, vol. 16(1), pages 1-15, January.
    2. 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.
    3. Fahad M. Almasoudi, 2023. "Grid Distribution Fault Occurrence and Remedial Measures Prediction/Forecasting through Different Deep Learning Neural Networks by Using Real Time Data from Tabuk City Power Grid," Energies, MDPI, vol. 16(3), pages 1-20, January.
    4. Qu, Guanghao & Li, Shengtao, 2023. "Atomic mechanisms of long-term pyrolysis and gas production in cellulose-oil composite for transformer insulation," Applied Energy, Elsevier, vol. 350(C).
    5. Wojciech Giernacki, 2022. "Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)," Energies, MDPI, vol. 15(20), pages 1-23, October.
    6. Przemyslaw Pietrzak & Piotr Pietrzak & Marcin Wolkiewicz, 2024. "Microcontroller-Based Embedded System for the Diagnosis of Stator Winding Faults and Unbalanced Supply Voltage of the Induction Motors," Energies, MDPI, vol. 17(2), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:17:p:6298-:d:900621. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.