A Distribution Line Fault Location Estimation Algorithm Based on Electromagnetic Time-Reversal Method Calculated in the Finite Difference Time Domain

Sun, Jian-he; Yang, Qing; Xu, Wilsun; He, Wenlong

doi:10.1109/temc.2021.3140170

Cited by 14 publications

(5 citation statements)

References 27 publications

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“…In practice, fault resistance can be affected by factors like fault type and surface contact. Usually, fault resistance can vary from several ohms to thousands of ohms [32,33]. With a soil resistivity of 100Ω•m, Figure 14 plot the identifier for the alpha modes.…”

Section: B the Fault Resistancementioning

confidence: 99%

Identification Method for Pulse Wave Resulted From Fault Transient of Overhead Transmission Line

Liu

Zhou

et al. 2023

IEEE Access

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This paper presents a simple and effective method to extract the identification quantity of transient pulse waveform produced by the overhead transmission line short-circuit fault. This method employs the second-order time harmonic equation of the power frequency component to convert the fault full-wave data. After the conversion, only the identification quantity remains. This method can amplify high-frequency spectral content by the times of the square of the angular frequency, and hence improve the sensitivity of identifying fault transient. Using the identification quantity, one can clearly observe the arrival time and relative intensity of fault transient. A three-phase half wavelength transmission line of 50Hz/3000km is simulated with PSCAD to illuminate the effectiveness of this method. The effects of fault type, soil resistivity, fault resistance, power frequency fluctuation, sampling rate, transposition length,fault inception angle and white noise are analyzed. Calculated results show that this method can distinguish different fault type and can work effectively at low sampling rate down to 10kHz. This method is helpful for improving the performance of the traveling-wave based fault location method.

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Section: B the Fault Resistancementioning

confidence: 99%

Identification Method for Pulse Wave Resulted From Fault Transient of Overhead Transmission Line

Liu

Zhou

et al. 2023

IEEE Access

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“…The modeling and simulation can be performed either in the frequency domain using analytical formulae [e.g., BLT equations Tesche et al (1996)] or in the time domain using time-domain simulation tools [e.g., the EMTP-RV as done in Mahseredjian et al (2007)] or the finite-difference timedomain (FDTD) method [e.g., Sun et al (2022a)]. Finally, the unknown fault location is determined using an appropriate criterion or metric to quantify the refocusing of the backwardpropagating waves.…”

Section: Electromagnetic Time Reversal Applied To Fault Location In P...mentioning

confidence: 99%

A review of time reversal-based methods applied to fault location in power networks

Wang

Razzaghi

et al. 2022

Front. Energy Res.

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Electromagnetic time reversal (EMTR)-based methods have been applied to the problem of fault location in power networks since 2012. Over the past decade, considerable theoretical investigations have led to a continuous improvement of the EMTR method in terms of fault location accuracy and efficiency. Meanwhile, different full-scale experiments, including pilot tests, were carried out, demonstrating the applicability and robustness of the EMTR method when applied to real power-system environments. This paper presents a review of the foregoing theoretical and experimental studies. Furthermore, suggestions for future studies are propounded, along with discussing various challenging research questions that still need to be addressed.

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“…The important thing is the prevention of these faults as much as possible and the mitigation of such faults [1]. 80% of faults occur in distribution lines; hence, this area is of particular importance for researchers [3]. Moreover, with the integration of renewable sources in the system like wind and solar, two-way power flows are introduced, adding to the complexity of a distribution system [4], and [5].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Fault Diagnosis in Power System Using AI: A Review

Nasim,

Aziz,

Qaiser

et al. 2024

SSURJET

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Electricity, which is essential to modern society, necessitates a consistent and uninterrupted supply. Faults in power systems pose difficulties, highlighting the vital importance of fault identification and diagnosis. This review paper provides a concise overview of artificial intelligence-based fault detection and diagnosis in power systems. The primary focus is on deep learning; on the one hand, it compares various works and acts as a primer for those who are unfamiliar with them. On the other hand, it delves into the application of UV-visible video processing to detect incipient faults by analyzing corona discharge and air ionization. Moreover, this state-of-the-art work highlights deep learning applications, particularly in UV-visible video processing, with the goal of detecting incipient faults through corona discharge and air ionization analysis. Despite ongoing research, the field lacks a clear path and structure, emphasizing the need for continued advancement in utilizing AI for effective fault detection and diagnosis in power systems.

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A Distribution Line Fault Location Estimation Algorithm Based on Electromagnetic Time-Reversal Method Calculated in the Finite Difference Time Domain

Cited by 14 publications

References 27 publications

Identification Method for Pulse Wave Resulted From Fault Transient of Overhead Transmission Line

Identification Method for Pulse Wave Resulted From Fault Transient of Overhead Transmission Line

A review of time reversal-based methods applied to fault location in power networks

Fault Detection and Fault Diagnosis in Power System Using AI: A Review

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