Abnormal state diagnosis method of current transformer based on wide area measurement

Yiling Tan; Xiaowei Yang; Linfeng Wu; Shunli Chen; Duan Kai

doi:10.1117/12.3024326

13 May 2024 Abnormal state diagnosis method of current transformer based on wide area measurement

Yiling Tan, Xiaowei Yang, Linfeng Wu, Shunli Chen, Duan Kai

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 1315955 (2024) https://doi.org/10.1117/12.3024326
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

Aiming at the problem that the correlation degree between various current transformer’s data is not analyzed when performing status detection in power system, a method of abnormal status diagnosis of current transformer based on wide area measurement is proposed. Firstly, topological analysis is carried out on the network structure of the substation by depth-first algorithm, and the regions to be detected are modeled. Then, the zero sequence current and positive sequence current of each node are calculated respectively according to the three-phase unbalance theorem. Finally, the data of all nodes are statistically analyzed by statistical principle. The abnormal current transformer is determined by comprehensively analyzing the correlation degree between the data. It makes full use of all node data in the measurement area, avoids detection of abnormal false positives caused by power system disturbance, and makes detection results more accurate and reliable. It has a certain guiding significance for practical engineering applications.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yiling Tan, Xiaowei Yang, Linfeng Wu, Shunli Chen, and Duan Kai "Abnormal state diagnosis method of current transformer based on wide area measurement", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 1315955 (13 May 2024); https://doi.org/10.1117/12.3024326

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