Application of Kernel Principal Component Analysis and Granger Cointegratance Algorithm in Accelerator Distribution Network

doi:10.1109/AINIT61980.2024.10581788

	Application of Kernel Principal Component Analysis and Granger Cointegratance Algorithm in Accelerator Distribution Network
	Kang, Shuai 1; Zhou, Zhongzu 2; Zhao, Jiang 1; Yan, Shaohui1 ; Jiang, Xinwei 1; Zhang, Shuxian 1
	2024
会议名称	5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024
会议录名称	2024 5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024
页码	1784-1789
会议日期	May 29, 2024 - May 31, 2024
会议地点	Hybrid, Nanjing, China
会议录编者/会议主办者	IEEE
出版者	Institute of Electrical and Electronics Engineers Inc.
摘要	The big data platform for accelerator distribution network deals with a large amount of data every moment during operation. Collecting and analyzing these data to identify hidden causal relationships within system makes it possible to effectively locate the root cause of system faults and improve system maintenance efficiency. Applying the methods of Kernel Principal Component Analysis (KPCA) and Granger Cointegratance (GC) to this platform, we first collect relevant information on power quality of accelerator distribution network under different faults conditions in order to understand changes between information before and after fault. Then, the KPCA method is used to extract the features of a large amount of fault data. Finally, the GC algorithm is utilized to identify the causal relationships between data from different locations. This paper would provide a effectual method for analysis, discovery and optimization of causes of faults in the accelerator distribution network. © 2024 IEEE.
关键词	Big data Fault detection Power quality Quality control Smart power grids Accelerator distribution network Causal relationships Data platform Faults detection Granger cointegratance Kernel fault detection Kernel principal component analyses (KPCA) Large amounts of data Principal-component analysis Smart grid
DOI	10.1109/AINIT61980.2024.10581788
收录类别	EI
语种	英语
EI入藏号	20243016749034
EI主题词	Principal component analysis
EI分类号	706.1 Electric Power Systems ; 706.1.2 Electric Power Distribution ; 723.2 Data Processing and Image Processing ; 913.3 Quality Assurance and Control ; 922.2 Mathematical Statistics
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://ir.nwnu.edu.cn/handle/39RV6HYL/98797
专题	实体学院_物理与电子工程学院
通讯作者	Zhao, Jiang
作者单位	1.School of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, China; 2.Electrical and Electromagnetic Compatibility Group, Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, China
第一作者单位	物理与电子工程学院
通讯作者单位	物理与电子工程学院
第一作者的第一单位	物理与电子工程学院
推荐引用方式 GB/T 7714	Kang, Shuai,Zhou, Zhongzu,Zhao, Jiang,et al. Application of Kernel Principal Component Analysis and Granger Cointegratance Algorithm in Accelerator Distribution Network[C]//IEEE:Institute of Electrical and Electronics Engineers Inc.,2024:1784-1789.