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A Distribution Line Fault Location Estimation Algorithm Based on Electromagnetic Time-Reversal Method Calculated in the Finite Difference Time Domain
Abstract
The distribution line is an important part of a power system. How to quickly eliminate faults is of great significance to improve the power supply reliability. Based on electromagnetic time-reversal (EMTR), this article proposes a fault location estimation algorithm calculated by the finite-difference time-domain (FDTD) method to solve two problems. One is the calculation efficiency of the fault current based method in the EMTR, and the other is the fault location accuracy of the EMTR-based method when the measurement device has a low sampling rate in the direct time. We first derived the FDTD updated equation of the node voltage, which is at the discontinuous point of the lossless single conductor transmission line. The algorithm obtains the fault current at any transverse branch of the distribution line through one-time simulation, which improves the calculation efficiency. Furthermore, considering the sampling rate of the transient signals, we analyzed the impact of the spectrum range of the transient signal, the interval of the guessed fault location, the length of the time-reversal window, the fault impedance, and the length of the line. The results showed that when the transient signal is measured at a high sampling rate (10 MHz), the proposed algorithm can be used to locate the fault with a minimum time-reversal window of 1 ms. When the sampling rate is as low as 100 kHz, the proposed algorithm can achieve accurate fault location estimation compared to the algorithm in the other time-domain electromagnetic transient simulation. The error of the proposed location algorithm is less than 100 m.
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“80% of faults occur in distribution lines; hence, this area is of particular importance for researchers [3].”
From Fault Detection and Fault Diagnosis in Power System Using AI: A Review (2024) · cites this paper as [3]
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Cited by
Sorted by citations
Fault Detection and Fault Diagnosis in Power System Using AI: A Review
Nasim, S. F., Aziz, S., Qaiser, A., Kulsoom, U., & Ahmed, S. (2024) · Sir Syed University Research Journal of Engineering & Technology · doi:10.33317/ssurj.598 · Cited by 17
Metadata
- Authors
- Sun, J., Yang, Q., Xu, W., & He, W.
- Affiliations
- Chongqing University (CN)
- Lijiang Huaping Power Supply Company of Yunnan Power Grid Company Ltd., Lijiang, China
- Yunnan Power Grid Co., Ltd. (China) (CN)
- Venue
- IEEE Transactions on Electromagnetic Compatibility
- Published
- 2022-06
- Volume
- 64
- Issue
- 3
- Pages
- 865-873
- ISSN
- 0018-9375 (ISSN-L)
- 1558-187X
- OpenAlex
- W4213339171
- Cited by
- 19
- References
- 0
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 7 · 2022: 3
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Electromagnetic Compatibility (publishedVersion)
- Funders
- National Natural Science Foundation of China (51477018)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Power Systems Fault Detection
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electromagnetic Simulation and Numerical Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrical Fault Detection and Protection
- Keywords
- electromagnetic time reversal, finite-difference time-domain simulation, short-circuit current calculation, fault impedance, power supply reliability
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