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; Guoming Ma

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2023.35498

Published Article

LAPSE:2023.35498

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, Guoming Ma

May 2, 2023

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.

Record ID

LAPSE:2023.35498

Keywords

adaptive noise reduction, joint diagnosis, k-nearest neighbors, reactor defect, relevance significance

Subject

Process Monitoring

Suggested Citation

Gao S, Xing C, Zhang Z, Xiang C, Liu H, Liu H, Shi R, Wang S, Ma G. Early Warning of High-Voltage Reactor Defects Based on Acoustic−Electric Correlation. (2023). LAPSE:2023.35498

Author Affiliations

Gao S: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Xing C: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Zhang Z: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Xiang C: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Liu H: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Liu H: State Grid Hebei Electric Power Co., Ltd., Electric Power Research Institute, Shijiazhuang 310014, China
Shi R: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China [ORCID]
Wang S: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Ma G: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China

Journal Name

Energies

Volume

15

Issue

19

First Page

7196

Year

2022

Publication Date

2022-09-30