SIA OpenIR  > 工业控制网络与系统研究室
A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics
Guo HF(郭海丰)1,2,3,5; Xu AD(徐皑冬)1,2; Wang K(王锴)1,2; Zhang XF(张晓峰)4; Han XJ(韩晓佳)3; Liu Y(刘洋)3; Lv DC(律德才)5
Department工业控制网络与系统研究室
Conference Name2018 Prognostics and System Health Management Conference
Conference DateOctober 26-28, 2018
Conference PlaceChongqing, China
Source Publication2018 Prognostics and System Health Management Conference
PublisherIEEE
Publication PlaceNew York
2018
Pages697-703
Indexed ByEI ; CPCI(ISTP)
EI Accession number20190806536833
WOS IDWOS:000459864800117
Contribution Rank1
ISSN2166-5656
Keyworddegeneration insulation failure PF resonant frequency premature prognostic
AbstractWhen the transformer works for a long time, its winding is gradually deteriorated with time, and the fault phenomena such as winding short circuit or circuit break lead to serious power supply accidents. Under high temperature conditions, this paper analyzes the degradation process of the winding, and determines that the resonant frequency can be used as testing index of its degradation process. Therefore, the resonant frequency is used to monitor the performance degradation state of the transformer winding and realize the advance prediction, which can effectively avoid accidents. Accurate prediction of system reliability is of plenty of importance to engineering systems for accomplishing the designate function and system safety management. As the concerned system is getting complicated and more sufficient health monitoring measurement is available, the traditional reliability prediction schemes resorting to only one kind of prediction approaches, model-based or data-driven, begin to show their limitations. This paper proposes a PF prognostic method by combining traditional model approaches. The effectiveness of the proposed method is verified by thermal degradation experiments. This method improves the reliability of power system and is conducive to the rapid development of smart grid.
Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/23826
Collection工业控制网络与系统研究室
Corresponding AuthorGuo HF(郭海丰)
Affiliation1.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences
2.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
3.University of Chinese Academy of Sciences, Beijing, China
4.Department of Electronic Systems Engineering, Hanyang University, Ansan, 15588, Korea
5.Liaoning Institute of Science and Technology, Benxi, China
Recommended Citation
GB/T 7714
Guo HF,Xu AD,Wang K,et al. A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics[C]. New York:IEEE,2018:697-703.
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