Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)

Partial Discharge Detection of High Voltage Electrical Equipment Based on Acoustic Imaging(AI)

Authors
DaoJun Luo1, *, Nan Yao1, YaTao Zhang1, Zhen Xian1
1Nanyang Power Supply Company of State Grid Henan Electric Power Company, Nanyang, Henan, 473006, China
*Corresponding author. Email: 13598265816@136.com
Corresponding Author
DaoJun Luo
Available Online 27 December 2022.
DOI
10.2991/978-94-6463-058-9_103How to use a DOI?
Keywords
Acoustic Imaging; High Voltage Electrical Equipment; Discharge Detection; Application of Acoustic Imaging
Abstract

With the development of market economy, high-voltage XLPE insulated conductor, as a key component of urban transmission line, plays an important role in maintaining the safe and stable operation of urban high-voltage line. The completion acceptance test of high-voltage conductor is a defect test before the investment and operation of high-voltage cable lines. It is an important link to prevent the investment and risk operation of high-voltage cable line. Based on the actual application of acoustic imaging technology in the partial discharge test of high-voltage distribution equipment, this paper puts forward the detection algorithm of high-voltage electrical equipment, tests the traditional AC withstand voltage method and the number of defective lines and acceptance lines when acoustic imaging is applied to the partial discharge detection method of high-voltage electrical equipment. The test results show that after the acoustic imaging proposed in this paper is applied to the partial discharge detection technology of high-voltage electrical equipment, the defect detection rate in the complete acceptance test of high-voltage cable is improved, while the number of operating lines with faults caused by small defects is reduced, which effectively reduces the number of high-voltage cables with faults caused by hidden dangers. It is verified that the application of acoustic imaging in partial discharge detection of high-voltage electrical equipment can effectively improve the fault detection rate of high-voltage cable completion acceptance and reduce the hidden dangers of high-voltage cable belts. The application of acoustic imaging in partial discharge detection of high voltage electrical equipment is the general trend in the future.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)
Series
Advances in Computer Science Research
Publication Date
27 December 2022
ISBN
978-94-6463-058-9
ISSN
2352-538X
DOI
10.2991/978-94-6463-058-9_103How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - DaoJun Luo
AU  - Nan Yao
AU  - YaTao Zhang
AU  - Zhen Xian
PY  - 2022
DA  - 2022/12/27
TI  - Partial Discharge Detection of High Voltage Electrical Equipment Based on Acoustic Imaging(AI)
BT  - Proceedings of the 2nd International Conference on Internet, Education and Information Technology (IEIT 2022)
PB  - Atlantis Press
SP  - 647
EP  - 653
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-058-9_103
DO  - 10.2991/978-94-6463-058-9_103
ID  - Luo2022
ER  -