An inrush current identification scheme for converter transformer based on magnetization curve model matching

Shiming Liu; Chao Zhang; Qiqi Zeng; Wenchen Zhao

doi:10.1117/12.2679573

18 July 2023 An inrush current identification scheme for converter transformer based on magnetization curve model matching

Shiming Liu, Chao Zhang, Qiqi Zeng, Wenchen Zhao

Author Affiliations +

Proceedings Volume 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023); 1272225 (2023) https://doi.org/10.1117/12.2679573
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 2023, Hangzhou, China

Abstract

Influenced by the complex environment on the AC/DC hybrid power system, converter transformer is susceptible to inrush current or DC bias interference, resulting in false blocking of differential protection during internal fault. In order to distinguish inrush current and internal fault accurately, this paper proposes an inrush current identification scheme for converter transformers based on the flux-current characteristic model matching method. Firstly, the magnetization curve model is selected to fit the nonlinear magnetization characteristics of the transformer, and a model matching scheme based on voltage comparison is designed. Then the model matching degree index (referred to as matching per unit value M in this paper) is constructed to quantitatively describe the model matching degree, and the inrush current identification criterion based on the model matching degree M is proposed. Finally, the action reliability of the proposed identification criterion is verified by simulation experiments.

Citation Download Citation

Shiming Liu, Chao Zhang, Qiqi Zeng, and Wenchen Zhao "An inrush current identification scheme for converter transformer based on magnetization curve model matching", Proc. SPIE 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 1272225 (18 July 2023); https://doi.org/10.1117/12.2679573

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KEYWORDS

Transformers

Reflection

Iron

Performance modeling

Reliability

Distortion

Intelligence systems

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