BAI B, YU Y Z, ZHAO H L, et al.Application of Optical Fiber Distributed Sensing System in Substation Maintenance[J]. Study on Optical Communications, 2023(1):53-57
BAI B, YU Y Z, ZHAO H L, et al.Application of Optical Fiber Distributed Sensing System in Substation Maintenance[J]. Study on Optical Communications, 2023(1):53-57 DOI: 10.13756/j.gtxyj.2023.01.006.
Application of Optical Fiber Distributed Sensing System in Substation Maintenance
if Gas Insulated Switchgear (GIS)equipment breaks down
it will lead to the suspension of substation power supply
and national production and life cannot be guaranteed. This paper discusses how to locate the breakdown of GIS efficiently and effectively. The Distributed Acoustic Sensing (DAS) system described in this paper is combined with Support Vector Machines (SVM) in machine learning method
which can realize fault location and facilitate fault elimination. At the same time
it is of great significance to restore the power supply of substation in time.
关键词
分布式声传感气体绝缘开关设备运行维护机器学习支持向量机
Keywords
DASGIS equipment operation and maintenancemachine learningSVM
references
Wang Z, Liu M, Yang X. Scaling Laws of the Lifting Height of a Conductive Particle in a Gas Insulated Switchgear[J]. Journal of Physics D: Applied Physics, 2021, 54(25): 255501.
Li N Y, Zhang H, Su X, et al. Power Supply Rapid Restoration Scheme of Gas Insulated Switchgear Bus Fault Trip[J]. Automation of Electric Power Systems, 2015, 39(22): 170-175.
Zhang Y J, Zhuang Y P, Cheng J. Analysis and Consideration of Malfunction of SF_6 GIS[J]. High Voltage Engineering, 2005(1): 89-90.
Daley M T, Freifeld M B, Ajo-Franklin J, et al. Field Testing of Fiber-optic Distributed Acoustic Sensing (DAS) for Subsurface Seismic Monitoring[J]. The Leading Edge, 2013, 32(6): 699-706.
Yu M, Zhang Y L, Xu Z C, et al. Signal Feature Extraction Method based on MEEMD-HHT for Distributed Optical Fiber Vibration Sensing System[J]. Infrared and Laser Engineering, 2021, 50(07): 200-211.
Zhang J, Du X M, He J J, et al. Characteristics of Fiber Distributed Acoustic Sensing System under Point-driven Single-frequency Oscillation[J]. Electro-Optic Technology Application, 2021, 36(4): 55-59.
Zhang X G. Introduction to Statistical Learning Theory and Support Vector Machines[J]. Acta Automatica Sinica, 2000, 26(1): 32-42.
Schuldt C, Laptev I, Caputo B. Recognizing Human Actions: a Local SVM Approach [C]// International Conference on Pattern Recognition. Cambridge, UK: IEEE, 2004: 1334462.
金梦茹. 基于结构响应向量与机器学习的损伤识别方法研究[D]. 广州: 华南理工大学, 2019.
Jin M R. Research on Damage Identification Method based on Structural Response Vector and Machine Learning[D]. GuangZhou: South China University of Technology, 2019.
Muda L, Begam M, Elamvazuthi I. Voice Recognition Algorithms Using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques[J]. Journal of Computing, 2010, 2(3): 138-143.
Beam Selection Algorithm in Millimeter Wave based on GCNet
Transfer Learning Assisted Transmission Quality Evaluation based on Machine Learning
Research on Modulation Format Recognition Algorithm based on Machine Learning
Transmission Performance Evaluation of Coherent Optical Communication System based on Machine Learning
Machine Learning based Intrusion Detection Strategy for SDON
Related Author
No data
Related Institution
. School of Communication and Information Engineering; b. Key Laboratory of New Generation Broadband Mobile Communication Technology, Chongqing University of Posts and Telecommunication
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications
The Ministry of Education Key Laboratory of Electromagnetic Wave Information Science, Department of Communication Science and Engineering, Fudan University
Peng Cheng Laboratory
Key Laboratory of FAST, National Astronomical Observatories, CAS