Literature DB >> 32075177

3D Printing Technique-Improved Phase-Sensitive OTDR for Breakdown Discharge Detection of Gas-Insulated Switchgear.

Zhen Chen1, Liang Zhang1, Huanhuan Liu1, Peng Peng2, Zhichao Liu1, Shi Shen1, Na Chen1, Shenhui Zheng2, Jian Li2, Fufei Pang1.   

Abstract

In this paper, we propose and demonstrate a gas-insulated switchgear (GIS) breakdown discharge detection system based on improved phase-sensitive optical time domain reflectometry (φ-OTDR) assisted by 3D-printed sensing elements. The sensing element is manufactured by a material with a high Poisson ratio for enhancement of the sensitivity of φ-OTDR to the acoustic emission detection during the breakdown discharge process. In our experiment, seven 3D-printed sensing elements incorporating with optical fibers are attached tightly onto the shell of the GIS, which are monitored by φ-OTDR to localize and detect the acoustic emission signal resulted from the breakdown discharge. Ultimately, thanks to the phase demodulation, acoustic signals induced by the breakdown discharge process can be captured and recovered. Furthermore, the time delay analysis of detected signals acquired by different sensing elements on the GIS breakdown discharge unit is able to distinguish the location of the insulation failure part in the GIS unit. It suggests that the φ-OTDR incorporated with 3D printing technology shows the advantage of robustness in GIS breakdown discharge monitoring and detection.

Entities:  

Keywords:  3D-printing technology; acoustic emission; breakdown discharge detection; optical fiber sensor; φ-OTDR

Year:  2020        PMID: 32075177     DOI: 10.3390/s20041045

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

1.  Application of Intensity-Based Coherent Optical Time Domain Reflectometry to Bridge Monitoring.

Authors:  Xin Lu; Sebastian Chruscicki; Marcus Schukar; Sven Münzenberger; Katerina Krebber
Journal:  Sensors (Basel)       Date:  2022-04-30       Impact factor: 3.576

2.  Real-Time Multi-Class Disturbance Detection for Φ-OTDR Based on YOLO Algorithm.

Authors:  Weijie Xu; Feihong Yu; Shuaiqi Liu; Dongrui Xiao; Jie Hu; Fang Zhao; Weihao Lin; Guoqing Wang; Xingliang Shen; Weizhi Wang; Feng Wang; Huanhuan Liu; Perry Ping Shum; Liyang Shao
Journal:  Sensors (Basel)       Date:  2022-03-03       Impact factor: 3.576

  2 in total

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