A Low‐Frequency Transformer Protection Method Based on Excitation Inductance Parameter Identification

ABSTRACT As the core hub equipment of offshore wind power low‐frequency transmission systems, low‐frequency transformers generate complex harmonic disturbances during internal faults, severely compromising the reliability of traditional current differential protection. To address this engineering challenge, this paper innovatively proposes a transformer fast main protection method based on excitation inductance parameter identification. Rooted in the unique application scenarios of offshore wind power, the research focuses on overcoming the limitations of existing ratio‐restraint differential protection constrained by magnetising inrush current identification. Specifically, the distinctive harmonic characteristics exhibited during low‐frequency transformer faults can invalidate second‐harmonic restraint principles. A novel identification model based on the dynamic characteristics of instantaneous excitation inductance is developed, which breaks through the limitations of traditional harmonic analysis methods and achieves precise discrimination between fault currents and magnetising inrush currents using single‐terminal current‐voltage data. Simulation experiments demonstrate that this method can reduce protection operation time to less than 10 ms, particularly suitable for special offshore platform conditions characterised by space constraints and maintenance difficulties. The proposed approach provides critical technical support for enhancing low‐frequency transformer protection in offshore wind farm grid‐connected low‐frequency transmission systems, demonstrating significant engineering application value.

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Publication Details

Journal
IET Electric Power Applications
Published
2025-01-01
DOI
https://doi.org/10.1049/elp2.70064
Primary Topic
Power Systems Fault Detection
Type
article
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article

A Low‐Frequency Transformer Protection Method Based on Excitation Inductance Parameter Identification

Yaming Ge, Shi Chen, Xiangping Kong, Zhe Quan et al.
IET Electric Power Applications
Power Systems Fault Detection
article

A Low‐Frequency Transformer Protection Method Based on Excitation Inductance Parameter Identification

Yaming Ge, Shi Chen, Xiangping Kong, Zhe Quan, Xueguang Wu, Shuping Gao, Chenqing Wang
article en

Abstract

ABSTRACT As the core hub equipment of offshore wind power low‐frequency transmission systems, low‐frequency transformers generate complex harmonic disturbances during internal faults, severely compromising the reliability of traditional current differential protection. To address this engineering challenge, this paper innovatively proposes a transformer fast main protection method based on excitation inductance parameter identification. Rooted in the unique application scenarios of offshore wind power, the research focuses on overcoming the limitations of existing ratio‐restraint differential protection constrained by magnetising inrush current identification. Specifically, the distinctive harmonic characteristics exhibited during low‐frequency transformer faults can invalidate second‐harmonic restraint principles. A novel identification model based on the dynamic characteristics of instantaneous excitation inductance is developed, which breaks through the limitations of traditional harmonic analysis methods and achieves precise discrimination between fault currents and magnetising inrush currents using single‐terminal current‐voltage data. Simulation experiments demonstrate that this method can reduce protection operation time to less than 10 ms, particularly suitable for special offshore platform conditions characterised by space constraints and maintenance difficulties. The proposed approach provides critical technical support for enhancing low‐frequency transformer protection in offshore wind farm grid‐connected low‐frequency transmission systems, demonstrating significant engineering application value.

IET Electric Power ApplicationsVol. 19(1)
Xi'an University of Science and Technology (CN), Electric Power Research Institute (US), Shanghai Electric (China) (CN), China XD Group (China) (CN)
Openalex Percentile: Top 79%
Power Systems Fault Detection
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A Low‐Frequency Transformer Protection Method Based on Excitation Inductance Parameter Identification — Yaming Ge, Shi Chen, et al. · IET Electric Power Applications (2025) | TGRS Research Map | TGRS