A fault location method for DC distribution networks with DC circuit breaker and current limiter coordination
To enhance the reliability of the protection system and accurately identify fault distances in DC networks, a fault location method is proposed, which leverages the coordination of existing DC circuit breakers (DCCBs) and current limiters. The proposed method effectively achieves single-end signal i...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | International Journal of Electrical Power & Energy Systems |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525002352 |
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| Summary: | To enhance the reliability of the protection system and accurately identify fault distances in DC networks, a fault location method is proposed, which leverages the coordination of existing DC circuit breakers (DCCBs) and current limiters. The proposed method effectively achieves single-end signal injection and identification by repurposing existing DCCB components and incorporating a specialized signal injection branch. Concurrently, an efficient cooperative control strategy for the components is introduced to enhance the integration of the fault location method. By analyzing the inductor current decay within the ranging circuit, the relationship between fault distance and fault resistance is established, leading to the development of a fault location algorithm. A DC distribution network model is built by PSCAD/EMTDC, and the location calculation method is simulated in MATLAB. The simulation results validate the efficacy of the proposed fault location method in accurately determining both the distance and resistance of faults. Moreover, this method has demonstrated its robustness across diverse cable types, noise levels, sampling frequencies, and resistance conditions. The proposed fault location method offers distinct advantages over existing techniques in terms of both accuracy and lower complexity of its construction.© 2017 Elsevier Inc. All rights reserved. |
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| ISSN: | 0142-0615 |