IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2852-d555356.html
   My bibliography  Save this article

Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation

Author

Listed:
  • Egnonnumi Lorraine Codjo

    (Centrale Lille, Arts Et Metiers Institute of Technology, University of Lille, JUNIA, ULR 2697-L2EP, F-59000 Lille, France
    Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

  • Bashir Bakhshideh Zad

    (Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

  • Jean-François Toubeau

    (Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

  • Bruno François

    (Centrale Lille, Arts Et Metiers Institute of Technology, University of Lille, JUNIA, ULR 2697-L2EP, F-59000 Lille, France)

  • François Vallée

    (Power Systems & Markets Research Group, Electrical Power Engineering Unit, University of Mons, B-7000 Mons, Belgium)

Abstract

Low voltage distribution networks have not been traditionally designed to accommodate the large-scale integration of decentralized photovoltaic (PV) generations. The bidirectional power flows in existing networks resulting from the load demand and PV generation changes as well as the influence of ambient temperature led to voltage variations and increased the leakage current through the cable insulation. In this paper, a machine learning-based framework is implemented for the identification of cable degradation by using data from deployed smart meter (SM) measurements. Nodal voltage variations are supposed to be related to cable conditions (reduction of cable insulation thickness due to insulation wear) and to client net demand changes. Various machine learning techniques are applied for classification of nodal voltages according to the cable insulation conditions. Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97.9% and 99.9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76.7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.

Suggested Citation

  • Egnonnumi Lorraine Codjo & Bashir Bakhshideh Zad & Jean-François Toubeau & Bruno François & François Vallée, 2021. "Machine Learning-Based Classification of Electrical Low Voltage Cable Degradation," Energies, MDPI, vol. 14(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2852-:d:555356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2852/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2852/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sin-Dong Kang & Jae-Ho Kim, 2020. "Investigation on the Insulation Resistance Characteristics of Low Voltage Cable," Energies, MDPI, vol. 13(14), pages 1-9, July.
    2. Jean-François Toubeau & Bashir Bakhshideh Zad & Martin Hupez & Zacharie De Grève & François Vallée, 2020. "Deep Reinforcement Learning-Based Voltage Control to Deal with Model Uncertainties in Distribution Networks," Energies, MDPI, vol. 13(15), pages 1-15, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bakhshideh Zad, Bashir & Toubeau, Jean-François & Bruninx, Kenneth & Vatandoust, Behzad & De Grève, Zacharie & Vallée, François, 2022. "Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments," Applied Energy, Elsevier, vol. 325(C).
    2. Gianfranco Chicco & Andrea Mazza & Salvatore Musumeci & Enrico Pons & Angela Russo, 2022. "Editorial for the Special Issue “Verifying the Targets—Selected Papers from the 55th International Universities Power Engineering Conference (UPEC 2020)”," Energies, MDPI, vol. 15(15), pages 1-8, August.
    3. Radel Sultanbekov & Ilia Beloglazov & Shamil Islamov & Muk Chen Ong, 2021. "Exploring of the Incompatibility of Marine Residual Fuel: A Case Study Using Machine Learning Methods," Energies, MDPI, vol. 14(24), pages 1-16, December.

    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. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    2. Xufei Ge & Fulin Fan & Martin J. Given & Brian G. Stewart, 2024. "Insulation Resistance Degradation Models of Extruded Power Cables under Thermal Ageing," Energies, MDPI, vol. 17(5), pages 1-22, February.
    3. Xiaowu Sun & Ying Qiao & Yinda Li & Chongfeng Cao & Shenrong Feng, 2024. "Insulation Resistance Characteristics of Dry DC Link Capacitors in the Presence of High Temperatures and Operating Voltages," Energies, MDPI, vol. 17(5), pages 1-16, February.
    4. Marius Florian Preduș & Cristinel Popescu & Eugen Răduca & Cornel Hațiegan, 2022. "Study of the Accelerated Degradation of the Insulation of Power Cables under the Action of the Acid Environment," Energies, MDPI, vol. 15(10), pages 1-10, May.
    5. Min Ho Kim & Hyun Jeong Seo & Sang Kyu Lee & Min Chul Lee, 2021. "Influence of Thermal Aging on the Combustion Characteristics of Cables in Nuclear Power Plants," Energies, MDPI, vol. 14(7), pages 1-17, April.
    6. Bashir Bakhshideh Zad & Jean-François Toubeau & François Vallée, 2021. "Chance-Constrained Based Voltage Control Framework to Deal with Model Uncertainties in MV Distribution Systems," Energies, MDPI, vol. 14(16), pages 1-16, August.
    7. Bakhshideh Zad, Bashir & Toubeau, Jean-François & Bruninx, Kenneth & Vatandoust, Behzad & De Grève, Zacharie & Vallée, François, 2022. "Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments," Applied Energy, Elsevier, vol. 325(C).

    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:14:y:2021:i:10:p:2852-:d:555356. 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.