IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i6p1004-d214044.html
   My bibliography  Save this article

Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines

Author

Listed:
  • Carlo Olivieri

    (UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy)

  • Francesco de Paulis

    (UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy)

  • Antonio Orlandi

    (UAq EMC Laboratory, Department of Industrial and Information Engineering and Economics, University of L’Aquila, 67100 L’Aquila, Italy)

  • Giorgio Giannuzzi

    (TERNA S.p.A., 00100 Rome, Italy)

  • Roberto Salvati

    (TERNA S.p.A., 00100 Rome, Italy)

  • Roberto Zaottini

    (TERNA S.p.A., 00100 Rome, Italy)

  • Carlo Morandini

    (TERNA S.p.A., 00100 Rome, Italy)

  • Lorenzo Mocarelli

    (TERNA S.p.A., 00100 Rome, Italy)

Abstract

This work presents the feasibility study of an on-line monitoring technique aimed to discover unwanted variations of longitudinal impedance along the line (also named “impedance discontinuities”) and, possibly, incipient faults typically occurring on high voltage power transmission lines, like those generated by oxidated midspan joints or bolted joints usually present on such lines. In this paper, the focus is placed on the application and proper customization of a technique based on the time-domain reflectometry (TDR) technique when applied to an in-service high-voltage overhead line. An extensive set of numerical simulations are provided in order to highlight the critical points of this particular application scenario, especially those that concern the modeling of both the TDR signal injection strategy and the required high-voltage coupling devices, and to plan a measurement activity. The modeling and simulation approach followed for the study of either the overhead line or the on-line TDR system is fully detailed, discussing three main strategies. Furthermore, some measurement data that were used to characterize the specific coupling device selected for this application at high frequency—that is, a capacitive voltage transformer (CVT)—are presented and discussed too. This work sets the basic concepts underlying the implementation of an on-line remote monitoring system based on reflectometric principles for in-service lines, showing how much impact is introduced by the high-voltage coupling strategy on the amplitude of the detected reflected voltage waves (also named “voltage echoes”).

Suggested Citation

  • Carlo Olivieri & Francesco de Paulis & Antonio Orlandi & Giorgio Giannuzzi & Roberto Salvati & Roberto Zaottini & Carlo Morandini & Lorenzo Mocarelli, 2019. "Remote Monitoring of Joints Status on In-Service High-Voltage Overhead Lines," Energies, MDPI, vol. 12(6), pages 1-17, March.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:1004-:d:214044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/6/1004/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/6/1004/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michal Wydra & Pawel Kubaczynski & Katarzyna Mazur & Bogdan Ksiezopolski, 2019. "Time-Aware Monitoring of Overhead Transmission Line Sag and Temperature with LoRa Communication," Energies, MDPI, vol. 12(3), pages 1-23, February.
    2. Li Zhang & Xiyue LuoYang & Yanjie Le & Fan Yang & Chun Gan & Yinxian Zhang, 2018. "A Thermal Probability Density–Based Method to Detect the Internal Defects of Power Cable Joints," Energies, MDPI, vol. 11(7), pages 1-13, June.
    3. Irfan Ullah & Fan Yang & Rehanullah Khan & Ling Liu & Haisheng Yang & Bing Gao & Kai Sun, 2017. "Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach," Energies, MDPI, vol. 10(12), pages 1-13, December.
    4. Fabio Massaro & Mariano Giuseppe Ippolito & Gaetano Zizzo & Giovanni Filippone & Andrea Puccio, 2018. "Methodologies for the Exploitation of Existing Energy Corridors. GIS Analysis and DTR Applications," Energies, MDPI, vol. 11(4), pages 1-15, April.
    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. Miren T. Bedialauneta & Igor Albizu & Elvira Fernandez & A. Javier Mazon, 2020. "Uncertainties in the Testing of the Coefficient of Thermal Expansion of Overhead Conductors," Energies, MDPI, vol. 13(2), pages 1-13, January.

    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. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    2. Lixiao Mu & Xiaobing Xu & Zhanran Xia & Bin Yang & Haoran Guo & Wenjun Zhou & Chengke Zhou, 2021. "Autonomous Analysis of Infrared Images for Condition Diagnosis of HV Cable Accessories," Energies, MDPI, vol. 14(14), pages 1-15, July.
    3. Arnaldo Rabello de Aguiar Vallim Filho & Daniel Farina Moraes & Marco Vinicius Bhering de Aguiar Vallim & Leilton Santos da Silva & Leandro Augusto da Silva, 2022. "A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case," Energies, MDPI, vol. 15(10), pages 1-41, May.
    4. Salvatore Favuzza & Mariano Giuseppe Ippolito & Fabio Massaro & Liliana Mineo & Rossano Musca & Gaetano Zizzo, 2018. "New Energy Corridors in the Euro-Mediterranean Area: The Pivotal Role of Sicily," Energies, MDPI, vol. 11(6), pages 1-14, June.
    5. Saez, Yago & Mochon, Asuncion & Corona, Luis & Isasi, Pedro, 2019. "Integration in the European electricity market: A machine learning-based convergence analysis for the Central Western Europe region," Energy Policy, Elsevier, vol. 132(C), pages 549-566.
    6. Alessandro Mingotti & Federica Costa & Lorenzo Peretto & Roberto Tinarelli & Paolo Mazza, 2021. "Modeling Stray Capacitances of High-Voltage Capacitive Dividers for Conventional Measurement Setups," Energies, MDPI, vol. 14(5), pages 1-15, February.
    7. Jing Li & Jinrui Tang & Xinze Wang & Binyu Xiong & Shenjun Zhan & Zilong Zhao & Hui Hou & Wanying Qi & Zhenhai Li, 2020. "Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network," Energies, MDPI, vol. 13(18), pages 1-21, September.
    8. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    9. Muhammad Rameez Javed & Zain Shabbir & Furqan Asghar & Waseem Amjad & Faisal Mahmood & Muhammad Omer Khan & Umar Siddique Virk & Aashir Waleed & Zunaib Maqsood Haider, 2022. "An Efficient Fault Detection Method for Induction Motors Using Thermal Imaging and Machine Vision," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    10. Rossana Coccia & Veronica Tonti & Chiara Germanò & Francesco Palone & Lorenzo Papi & Lorenzo Ricciardi Celsi, 2022. "A Multi-Variable DTR Algorithm for the Estimation of Conductor Temperature and Ampacity on HV Overhead Lines by IoT Data Sensors," Energies, MDPI, vol. 15(7), pages 1-13, April.
    11. Nikolay Lysov & Alexander Temnikov & Leonid Chernensky & Alexander Orlov & Olga Belova & Tatiana Kivshar & Dmitry Kovalev & Vadim Voevodin, 2021. "Artificial Negative Polarity Thunderstorm Cell Modeling of Nearby Incomplete Upward Discharges’ Influence on Elements of Monitoring Systems for Air Transmission Lines," Energies, MDPI, vol. 14(21), pages 1-17, October.
    12. Osni Silva Junior & Jose Carlos Pereira Coninck & Fabiano Gustavo Silveira Magrin & Francisco Itamarati Secolo Ganacim & Anselmo Pombeiro & Leonardo Göbel Fernandes & Eduardo Félix Ribeiro Romaneli, 2023. "Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model," Energies, MDPI, vol. 16(11), pages 1-15, May.
    13. Davide Della Giustina & Stefano Rinaldi & Stefano Robustelli & Andrea Angioni, 2021. "Massive Generation of Customer Load Profiles for Large Scale State Estimation Deployment: An Approach to Exploit AMI Limited Data," Energies, MDPI, vol. 14(5), pages 1-26, February.
    14. Olcay Özge Ersöz & Ali Fırat İnal & Adnan Aktepe & Ahmet Kürşad Türker & Süleyman Ersöz, 2022. "A Systematic Literature Review of the Predictive Maintenance from Transportation Systems Aspect," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    15. Virginia Negri & Alessandro Mingotti & Roberto Tinarelli & Lorenzo Peretto, 2023. "Comparison of Algorithms for the AI-Based Fault Diagnostic of Cable Joints in MV Networks," Energies, MDPI, vol. 16(1), pages 1-20, January.
    16. Jiahong He & Kang He & Longfei Cui, 2019. "Charge-Simulation-Based Electric Field Analysis and Electrical Tree Propagation Model with Defects in 10 kV XLPE Cable Joint," Energies, MDPI, vol. 12(23), pages 1-22, November.
    17. Moamin A. Mahmoud & Naziffa Raha Md Nasir & Mathuri Gurunathan & Preveena Raj & Salama A. Mostafa, 2021. "The Current State of the Art in Research on Predictive Maintenance in Smart Grid Distribution Network: Fault’s Types, Causes, and Prediction Methods—A Systematic Review," Energies, MDPI, vol. 14(16), pages 1-27, August.
    18. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    19. Irfan Ullah & Rehan Ullah Khan & Fan Yang & Lunchakorn Wuttisittikulkij, 2020. "Deep Learning Image-Based Defect Detection in High Voltage Electrical Equipment," Energies, MDPI, vol. 13(2), pages 1-17, January.
    20. Andreas Anael Pereira Gomes & Francisco Itamarati Secolo Ganacim & Fabiano Gustavo Silveira Magrin & Nara Bobko & Leonardo Göbel Fernandes & Anselmo Pombeiro & Eduardo Félix Ribeiro Romaneli, 2023. "A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models," Data, MDPI, vol. 8(7), pages 1-16, July.

    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:12:y:2019:i:6:p:1004-:d:214044. 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.