Early Warning of High-Voltage Reactor Defects Based on Acoustic–Electric Correlation

Shuguo Gao, Chao Xing (), Zhigang Zhang, Chenmeng Xiang, Haoyu Liu, Hongliang Liu, Rongbin Shi, Sihan Wang and Guoming Ma ()
Additional contact information
Shuguo Gao: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Chao Xing: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Zhigang Zhang: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Chenmeng Xiang: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Haoyu Liu: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Hongliang Liu: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Rongbin Shi: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Sihan Wang: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Guoming Ma: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Energies, 2022, vol. 15, issue 19, 1-11

Abstract: Traditional high-voltage reactor monitoring and diagnosis research has problems such as high sampling demand, difficulty in noise reduction on site, many false alarms, and lack of on-site data. In order to solve the above problems, this paper proposes an acoustic–electric fusion high-voltage reactor acquisition system and defect diagnosis method based on reactor pulse current and ultrasonic detection signal. Using the envelope peak signal as the basic detection data, the sampling requirement of the system is reduced. To fill the missing data with partial discharge (PD) information, a method based on k-nearest neighbor (KNN) is proposed. An adaptive noise reduction method is carried out, and a noise threshold calculation method is given for the field sensors. A joint analysis method of acoustic and electrical signals based on correlation significance is established to determine whether a discharge event has occurred based on correlation significance. Finally, the method is applied to a UHV reactor on the spot, which proves the effectiveness of the method proposed in this paper.

Keywords: relevance significance; reactor defect; joint diagnosis; k-nearest neighbors; adaptive noise reduction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/7196/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7196/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:7196-:d:929998

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().