IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i20p6621-d655682.html
   My bibliography  Save this article

Investigation of Factors Affecting Partial Discharges on Epoxy Resin: Simulation, Experiments, and Reference on Electrical Machines

Author

Listed:
  • Dimosthenis Verginadis

    (Department of Electrical & Computer Engineering, Democritus University of Thrace (DUTh), 671 00 Xanthi, Greece)

  • Athanasios Karlis

    (Department of Electrical & Computer Engineering, Democritus University of Thrace (DUTh), 671 00 Xanthi, Greece)

  • Michael G. Danikas

    (Department of Electrical & Computer Engineering, Democritus University of Thrace (DUTh), 671 00 Xanthi, Greece)

  • Jose A. Antonino-Daviu

    (Instituto Tecnologico de la Energia, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

In Power Systems, Synchronous Generators (SGs) are mostly used for generating electricity. Their insulation system, of which epoxy resin is a core component, plays a significant role in reliable operation. Epoxy resin has high mechanical strength, a characteristic that makes it a very good material for reliable SG insulation. Partial Discharges (PDs) are a constant threat to this insulation since they cause deterioration and consequential degradation of the aforementioned material. Therefore, it is very important to detect PDs, as they are both a symptom of insulation deterioration and a means to identify possible faults. Offline and Online PDs Tests are described, and a MATLAB/Simulink model, which simulates the capacitive model of PDs, is presented in this paper. Moreover, experiments are carried out in order to examine how the flashover voltage of epoxy resin samples is affected by different humidity levels. The main purpose of this manuscript is to investigate factors, such as the applied voltage, number, and volume of water droplets and water conductivity, which affect the condition of epoxy resin, and how these are related to PDs and flashover voltages, which may appear also in electrical machines’ insulation. The aforementioned factors may affect the epoxy resin, resulting in an increase in PDs, which in turn increases the overall Electrical Rotating Machines (EMs) risk factor.

Suggested Citation

  • Dimosthenis Verginadis & Athanasios Karlis & Michael G. Danikas & Jose A. Antonino-Daviu, 2021. "Investigation of Factors Affecting Partial Discharges on Epoxy Resin: Simulation, Experiments, and Reference on Electrical Machines," Energies, MDPI, vol. 14(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6621-:d:655682
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/20/6621/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/20/6621/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanlin Luo & Zhaohui Li & Hong Wang, 2017. "A Review of Online Partial Discharge Measurement of Large Generators," Energies, MDPI, vol. 10(11), pages 1-32, October.
    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. Janjanam Naveen & Myneni Sukesh Babu & Ramanujam Sarathi & Ramachandran Velmurugan & Michael G. Danikas & Athanasios Karlis, 2021. "Investigation on Electrical and Thermal Performance of Glass Fiber Reinforced Epoxy–MgO Nanocomposites," Energies, MDPI, vol. 14(23), pages 1-17, November.

    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. Ghulam Amjad Hussain & Ashraf A. Zaher & Detlef Hummes & Madia Safdar & Matti Lehtonen, 2020. "Hybrid Sensing of Internal and Surface Partial Discharges in Air-Insulated Medium Voltage Switchgear," Energies, MDPI, vol. 13(7), pages 1-16, April.
    2. Sara Mantach & Ahmed Ashraf & Hamed Janani & Behzad Kordi, 2021. "A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set," Energies, MDPI, vol. 14(5), pages 1-16, March.
    3. Christian Gianoglio & Edoardo Ragusa & Paolo Gastaldo & Federico Gallesi & Francesco Guastavino, 2021. "Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks," Energies, MDPI, vol. 14(15), pages 1-23, August.
    4. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.
    5. Christian Gianoglio & Edoardo Ragusa & Andrea Bruzzone & Paolo Gastaldo & Rodolfo Zunino & Francesco Guastavino, 2020. "Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus," Energies, MDPI, vol. 13(5), pages 1-16, March.
    6. Anderson J. C. Sena & Rodrigo M. S. de Oliveira & Júlio A. S. do Nascimento, 2021. "Frequency Resolved Partial Discharges Based on Spectral Pulse Counting," Energies, MDPI, vol. 14(21), pages 1-36, October.
    7. Fang Dao & Yun Zeng & Yidong Zou & Xiang Li & Jing Qian, 2021. "Acoustic Vibration Approach for Detecting Faults in Hydroelectric Units: A Review," Energies, MDPI, vol. 14(23), pages 1-16, November.
    8. Muhammad Shafiq & Ivar Kiitam & Kimmo Kauhaniemi & Paul Taklaja & Lauri Kütt & Ivo Palu, 2020. "Performance Comparison of PD Data Acquisition Techniques for Condition Monitoring of Medium Voltage Cables," Energies, MDPI, vol. 13(16), pages 1-14, August.
    9. Donny Soh & Sivaneasan Bala Krishnan & Jacob Abraham & Lai Kai Xian & Tseng King Jet & Jimmy Fu Yongyi, 2022. "Partial Discharge Diagnostics: Data Cleaning and Feature Extraction," Energies, MDPI, vol. 15(2), pages 1-12, January.
    10. Dmitry A. Ivanov & Marat F. Sadykov & Danil A. Yaroslavsky & Aleksandr V. Golenishchev-Kutuzov & Tatyana G. Galieva, 2021. "Non-Contact Methods for High-Voltage Insulation Equipment Diagnosis during Operation," Energies, MDPI, vol. 14(18), pages 1-16, September.
    11. Alexander S. Karandaev & Igor M. Yachikov & Andrey A. Radionov & Ivan V. Liubimov & Nikolay N. Druzhinin & Ekaterina A. Khramshina, 2022. "Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition," Energies, MDPI, vol. 15(10), pages 1-21, May.
    12. Jonathan dos Santos Cruz & Fabiano Fruett & Renato da Rocha Lopes & Fabio Luiz Takaki & Claudia de Andrade Tambascia & Eduardo Rodrigues de Lima & Mateus Giesbrecht, 2022. "Partial Discharges Monitoring for Electric Machines Diagnosis: A Review," Energies, MDPI, vol. 15(21), pages 1-31, October.
    13. Krzysztof Walczak, 2023. "Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors," Energies, MDPI, vol. 16(5), pages 1-15, February.

    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:20:p:6621-:d:655682. 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.