IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i5p2297-d1082285.html
   My bibliography  Save this article

Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors

Author

Listed:
  • Krzysztof Walczak

    (Institute of Electric Power Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

The issue of detecting and locating defects generating partial discharges (PDs) is very important for the proper functioning of power grids. Despite the existence of many localization methods, both very large and relatively small objects are still a challenge due to the problem of obtaining the required measurement accuracy. This article presents the idea of the method of PD localization in small objects of simple structure with the use of a system of four capacitive probes. Based on the relative difference in the amplitudes of the signals recorded by the pair of capacitive sensors and considering their distance characteristics, it is possible to determine the place where the PD pulses are generated. In the example of measurements made on a support insulator, it was shown that the location of a defect using the proposed method allows for an indication accuracy of up to 0.5 cm.

Suggested Citation

  • Krzysztof Walczak, 2023. "Localization of HV Insulation Defects Using a System of Associated Capacitive Sensors," Energies, MDPI, vol. 16(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2297-:d:1082285
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/5/2297/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/5/2297/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wojciech Sikorski & Krzysztof Walczak & Wieslaw Gil & Cyprian Szymczak, 2020. "On-Line Partial Discharge Monitoring System for Power Transformers Based on the Simultaneous Detection of High Frequency, Ultra-High Frequency, and Acoustic Emission Signals," Energies, MDPI, vol. 13(12), pages 1-37, June.
    2. Krzysztof Walczak & Wojciech Sikorski, 2021. "Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System," Energies, MDPI, vol. 14(18), pages 1-20, September.
    3. Hamidreza Karami & Farzane Askari & Farhad Rachidi & Marcos Rubinstein & Wojciech Sikorski, 2022. "An Inverse-Filter-Based Method to Locate Partial Discharge Sources in Power Transformers," Energies, MDPI, vol. 15(6), pages 1-21, March.
    4. Zbigniew Nadolny, 2022. "Determination of Dielectric Losses in a Power Transformer," Energies, MDPI, vol. 15(3), pages 1-14, January.
    5. 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.
    6. Zbigniew Nadolny, 2022. "Electric Field Distribution and Dielectric Losses in XLPE Insulation and Semiconductor Screens of High-Voltage Cables," Energies, MDPI, vol. 15(13), pages 1-14, June.
    7. Marek Florkowski, 2020. "Classification of Partial Discharge Images Using Deep Convolutional Neural Networks," Energies, MDPI, vol. 13(20), pages 1-17, 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. Zbigniew Nadolny, 2023. "Design and Optimization of Power Transformer Diagnostics," Energies, MDPI, vol. 16(18), pages 1-7, September.

    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. Daria Wotzka & Wojciech Sikorski & Cyprian Szymczak, 2022. "Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning," Energies, MDPI, vol. 15(9), pages 1-20, April.
    2. 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.
    3. 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.
    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. Zbigniew Nadolny, 2022. "Impact of Changes in Limit Values of Electric and Magnetic Field on Personnel Performing Diagnostics of Transformers," Energies, MDPI, vol. 15(19), pages 1-15, October.
    6. Gustavo de Oliveira Machado & Luciano Coutinho Gomes & Augusto Wohlgemuth Fleury Veloso da Silveira & Carlos Eduardo Tavares & Darizon Alves de Andrade, 2022. "Impacts of Harmonic Voltage Distortions on the Dynamic Behavior and the PRPD Patterns of Partial Discharges in an Air Cavity Inside a Solid Dielectric Material," Energies, MDPI, vol. 15(7), pages 1-20, April.
    7. Franciszek Witos & Aneta Olszewska & Zbigniew Opilski & Agnieszka Lisowska-Lis & Grzegorz Szerszeń, 2020. "Application of Acoustic Emission and Thermal Imaging to Test Oil Power Transformers," Energies, MDPI, vol. 13(22), pages 1-20, November.
    8. 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.
    9. Jinseok Kim & Ki-Il Kim, 2021. "Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
    10. 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.
    11. Shaorui Qin & Siyuan Zhou & Taiyun Zhu & Shenglong Zhu & Jianlin Li & Zheran Zheng & Shuo Qin & Cheng Pan & Ju Tang, 2021. "Sinusoidal Noise Removal in PD Measurement Based on Synchrosqueezing Transform and Singular Spectrum Analysis," Energies, MDPI, vol. 14(23), pages 1-22, November.
    12. Przemyslaw Goscinski & Zbigniew Nadolny & Andrzej Tomczewski & Ryszard Nawrowski & Tomasz Boczar, 2023. "The Influence of Heat Transfer Coefficient α of Insulating Liquids on Power Transformer Cooling Systems," Energies, MDPI, vol. 16(6), pages 1-15, March.
    13. Franciszek Witos & Aneta Olszewska, 2023. "Investigation of Partial Discharges within Power Oil Transformers by Acoustic Emission," Energies, MDPI, vol. 16(9), pages 1-20, April.
    14. 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.
    15. Krzysztof Walczak & Wojciech Sikorski, 2021. "Non-Contact High Voltage Measurement in the Online Partial Discharge Monitoring System," Energies, MDPI, vol. 14(18), pages 1-20, September.
    16. 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.
    17. 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.
    18. Marek Florkowski, 2021. "Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns," Energies, MDPI, vol. 14(13), pages 1-18, June.
    19. Jianfeng Zheng & Zhichao Chen & Qun Wang & Hao Qiang & Weiyue Xu, 2022. "GIS Partial Discharge Pattern Recognition Based on Time-Frequency Features and Improved Convolutional Neural Network," Energies, MDPI, vol. 15(19), pages 1-14, October.
    20. 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.

    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:16:y:2023:i:5:p:2297-:d:1082285. 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.