A Fault Direction Discrimination Method for a Two-Terminal Weakly Fed AC System Using the Time-Domain Fault Model for the Difference Discrimination of Composite Electrical Quantities

The project of the flexible direct transmission of renewable energy has become an inevitable development trend for the large-scale grid connection of renewable energy. Its two-terminal weakly fed AC system is often composed of 100% power electronic equipment, which leads to an essential transformation in fault characteristics and protection requirements. At present, in research, the traditional directional elements are limited by the negative-sequence control strategy, resulting in the decline of their sensitivity and reliability. Therefore, this paper proposes a model for identifying directional elements using composite electrical quantities that is not affected by the control strategy of the two-terminal weakly fed AC system and can reliably identify the fault direction. Firstly, the adaptability of traditional directional elements under the negative-sequence current suppression strategy on both sides of the system when faults occur in the AC line was analyzed. Secondly, based on the idea of model recognition, the model relationship of fault voltage and current in the case of ground faults and non-ground faults occurring at different locations was analyzed. Finally, a fitted voltage was constructed and the Kendall correlation coefficient was introduced to achieve fault direction discrimination. Simulation results demonstrate that the proposed pilot protection scheme can operate reliably under conditions of 300 Ω transition resistance and 25 dB noise interference.

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Publication Details

Journal
Electronics
Published
2025-06-24
DOI
https://doi.org/10.3390/electronics14132556
Primary Topic
Power Systems Fault Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

A Fault Direction Discrimination Method for a Two-Terminal Weakly Fed AC System Using the Time-Domain Fault Model for the Difference Discrimination of Composite Electrical Quantities

Junjie Hou, Yifan Zhao, Wentao Yang, Lie Li et al.
Electronics
Power Systems Fault Detection
article

A Fault Direction Discrimination Method for a Two-Terminal Weakly Fed AC System Using the Time-Domain Fault Model for the Difference Discrimination of Composite Electrical Quantities

Junjie Hou, Yifan Zhao, Wentao Yang, Lie Li, Ping Xiong, Xiaoqian Zhu, Yu Sun
article en

Abstract

The project of the flexible direct transmission of renewable energy has become an inevitable development trend for the large-scale grid connection of renewable energy. Its two-terminal weakly fed AC system is often composed of 100% power electronic equipment, which leads to an essential transformation in fault characteristics and protection requirements. At present, in research, the traditional directional elements are limited by the negative-sequence control strategy, resulting in the decline of their sensitivity and reliability. Therefore, this paper proposes a model for identifying directional elements using composite electrical quantities that is not affected by the control strategy of the two-terminal weakly fed AC system and can reliably identify the fault direction. Firstly, the adaptability of traditional directional elements under the negative-sequence current suppression strategy on both sides of the system when faults occur in the AC line was analyzed. Secondly, based on the idea of model recognition, the model relationship of fault voltage and current in the case of ground faults and non-ground faults occurring at different locations was analyzed. Finally, a fitted voltage was constructed and the Kendall correlation coefficient was introduced to achieve fault direction discrimination. Simulation results demonstrate that the proposed pilot protection scheme can operate reliably under conditions of 300 Ω transition resistance and 25 dB noise interference.

ElectronicsVol. 14(13)
State Grid Corporation of China (China) (CN), Xinjiang University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 84%
Power Systems Fault Detection
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