A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics
文献类型：会议论文
| 作者 | Zhang XF(张晓峰)3; Liu Y(刘洋)2; Han XJ(韩晓佳)2 ; Lv DC(律德才)1; Guo HF(郭海丰)1,2,4,5; Xu AD(徐皑冬)4,5; Wang K(王锴)4,5
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| 出版日期 | 2018 |
| 会议日期 | October 26-28, 2018 |
| 会议地点 | Chongqing, China |
| 关键词 | degeneration insulation failure PF resonant frequency premature prognostic |
| 页码 | 697-703 |
| 英文摘要 | When 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. |
| 产权排序 | 1 |
| 会议录 | 2018 Prognostics and System Health Management Conference
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISSN号 | 2166-5656 |
| WOS记录号 | WOS:000459864800117 |
| 源URL | [http://ir.sia.cn/handle/173321/23826]  |
| 专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
| 通讯作者 | Guo HF(郭海丰) |
| 作者单位 | 1.Liaoning Institute of Science and Technology, Benxi, China 2.University of Chinese Academy of Sciences, Beijing, China 3.Department of Electronic Systems Engineering, Hanyang University, Ansan, 15588, Korea 4.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China 5.Key Laboratory of Networked Control Systems, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Zhang XF,Liu Y,Han XJ,et al. A Particle Filtering Based Approach for Transformer Winding Degradation Prognostics[C]. 见:. Chongqing, China. October 26-28, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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