Method of Online Status Monitoring for Windings of Three-winding Transformer Based on Improved Parameter Identification

被引：0
|
作者：
Chen Y. [1 ]
Liang J. [1 ]
Zhang J. [2 ]
Yu J. [2 ]
Zhang L. [1 ]
机构：
[1] Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education, Shandong University, Jinan
[2] Power Dispatch and Control Center, China Southern Power Grid Co., Ltd., Guangzhou
来源：
Gaodianya Jishu/High Voltage Engineering | 2019年 / 45卷 / 05期
关键词：
Big data platform; Condition number; Parameter identification; Short-circuit reactance; Transformer; Winding condition assessment;
D O I：
10.13336/j.1003-6520.hve.20190430029
中图分类号：
学科分类号：
摘要：
The electrical parameters identification is an important means to realize the winding condition monitoring of power transformers. Therefore, was proposed a method of on-line monitoring based on short-circuit reactance identification for three-winding transformer. In order to improve the accuracy of parameter identification, we investigated the relationship between different load distributions of the transformer and the condition number of the coefficient matrix. Using the condition number as a criterion, we adjusted the number of solution parameters according to different load states of the transformer. Furthermore, relying on the big data platform, we established an on-line monitoring method for transformer windings by reasonable screening data and identification scheme adaptive matching. Finally, numerical simulation was used to verify the influence of condition number on accuracy of parameter identification, while the engineering data were used to demonstrate the performance of on-line monitoring. The results show that the condition number of the coefficient matrix has an obvious influence on the accuracy of parameter identification, where the condition number is positively correlated with the error of the identification result. At the same time, the analysis of the actual engineering data by this method is consistent with the real state of the transformer. The results of this study can provide references for parameter identification and on-line status monitoring of three-winding transformer. © 2019, High Voltage Engineering Editorial Department of CEPRI. All right reserved.
引用
收藏
页码：1567 / 1575
页数：8
相关论文
共 21 条
  • [1] Sun X., He W., Zhan J., Et al., Current status and development of test and diagnostic technique of transformer winding deformation, High Voltage Engineering, 42, 4, pp. 1207-1220, (2016)
  • [2] Bagheri M., Naderi M.S., Blackburn T., Advanced transformer winding deformation diagnosis: moving from off-line to on-line, IEEE Transactions on Dielectrics and Electrical Insulation, 19, 6, pp. 1860-1870, (2012)
  • [3] Li G., Yu C., Liu Y., Et al., Challenges and prospects of fault prognostic and health management for power transformer, Automation of Electric Power Systems, 41, 23, pp. 156-162, (2017)
  • [4] Zheng M., Yu J., Chen H., Et al., Design of fault warning model of transmission line based on big data, Southern Power System Technology, 11, 4, pp. 30-36, (2017)
  • [5] Li J., Liang J., Yun Z., Et al., Probability analysis of progressive changes of equipment state based on electrical information, Transactions of China Electrotechnical Society, 28, 10, pp. 355-358, (2013)
  • [6] Hong K.X., Huang H., Zhou J.P., Winding condition assessment of power transformers based on vibration correlation, IEEE Transactions on Power Delivery, 30, 4, pp. 1735-1742, (2015)
  • [7] Zhang Z., Wang Y., Zhang L., Et al., Research on an online monitoring device for transformer winding deformation using NICS pulse injection method, High Voltage Engineering, 41, 7, pp. 2259-2266, (2015)
  • [8] Yao C., Zhao Z., Li C., Et al., Online detecting winding deformation of power transformer based on transientovervoltage, High Voltage Engineering, 41, 3, pp. 873-877, (2015)
  • [9] Deng X., Xiong X., Gao L., Et al., Method of on-line monitoring of transformer winding deformation based on parameter identification, Proceedings of the CSEE, 34, 28, pp. 4950-4958, (2014)
  • [10] Abu-Siada A., Islam S., A novel online technique to detect power transformer winding faults, IEEE Transactions on Power Delivery, 27, 2, pp. 849-857, (2012)
123