[1]曾思嘉,方瑞明,彭长青,等.全景动态网络标志物的汽轮发电机定子绕组热故障预警[J].华侨大学学报(自然科学版),2025,(2):201-208.[doi:10.11830/ISSN.1000-5013.202409021]
　ZENG Sijia,FANG Ruiming,PENG Changqing,et al.Early Warning of Thermal Fault in Turbine Generator Stator Winding of Landscape Dynamic Network Marker[J].Journal of Huaqiao University(Natural Science),2025,(2):201-208.[doi:10.11830/ISSN.1000-5013.202409021]
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全景动态网络标志物的汽轮发电机定子绕组热故障预警()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
期数:
2025年第2期
页码:
201-208
栏目:
出版日期:
2025-03-20

文章信息/Info

Title:
Early Warning of Thermal Fault in Turbine Generator Stator Winding of Landscape Dynamic Network Marker
文章编号:
1000-5013(2025)02-0201-08
作者:
曾思嘉;  方瑞明;  彭长青;  庄杰农;  尚荣艳
华侨大学 信息科学与工程学院, 福建 厦门 361021
Author(s):
ZENG Sijia;  FANG Ruiming;  PENG Changqing; ZHUANG Jienong;  SHANG Rongyan
College of Information Science and Engineering, Huaqiao University, Xiamen 361021, China
关键词:
汽轮发电机;  定子绕组;  热故障;  全景动态网络标志物;  故障检测;  故障定位
Keywords:
steam turbine generator;  stator winding;  thermal fault;  landscape dynamic network marker;  fault detection;  fault localization
分类号:
TM311
DOI:
10.11830/ISSN.1000-5013.202409021
文献标志码:
A
摘要:
将汽轮发电机组的集散控制系统(DCS)的定子各槽出水口水温监测点映射为复杂网络中的节点,从而能够基于汽轮发电机DCS监测数据对定子绕组的热状态进行观测。根据DCS监测数据的时序特性,引入全景动态网络标志物(L-DNM)法计算网络中各节点的特异性皮尔逊相关系数,以构建不同采样时刻的特异性差分网络。量化网络中各节点的动态变化以进行故障预警,进而筛选出温度异常升高的关键节点,根据这些关键节点构建动态网络标志物(DNM)以识别故障位置。结果表明:文中方法能够实现对早期故障的预警和异常槽口位置的定位。
Abstract:
The outlet water temperature monitoring points for each stator slot in the steam turbine generator group distributed control system(DCS)are mapped to nodes in a complex network, the thermal state of the stator windings can be observed based on the steam turbine generator DCS monitoring data. Based on the time series characteristics of the DCS monitoring data, the landscape dynamic network marker(L-DNM)method is introduced to calculate the specific Pearson correlation coefficients of each node in the network to construct specific differential networks at different sampling times. The dynamic changes of each node in the network are quantified for the purpose of fault prediction. Subsequently, critical nodes with abnormal increasing temperature are identified to construct a dynamical network marker(DNM)for fault location identification. The results show that the proposed method can achieve early warning of faults and localization of abnormal slot positions.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2024-09-02
通信作者: 方瑞明(1972-),男,教授,博士,主要从事电气设备在线监测与故障诊断的研究。E-mail:fangrm@hqu.edu.cn。
基金项目: 国家自然科学基金资助项目(52477048); 福建省高校产学合作项目(2024H6009); 福建省厦门市自然科学基金资助项目(3502Z202373952); 福建省厦门市产学研项目(2023CXY0201)
更新日期/Last Update: 2025-03-20