Arcing fault diagnosis using first peak arrival of EM radiation signal

Amirruddin, Melaty; Adzman, Mohd Rafi; Affendi, Nur Adyani Mohd; Azmi, Azwan Iskandar; Idris, Muhd Hafizi; Halim, Hana Abdull; Isa, Muzamir; Halim, Syahirah Abdul

doi:10.1088/1742-6596/1878/1/012041

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…In recent times, there has been significant attention to arcing source detection in location-based technologies [1]. Under actual operating conditions, the presence of an arcing fault could significantly contribute to service interruption [4]. Research has generally been conducted on arcing fault detection, such as in [5] [6] [7][8] [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, noisy signals contribute to inaccurate results due to the presence of interference that may cause disturbances in dominant arcing signals. Discrete spectral interference (DSI) from radio broadcast signal and white Gaussian noise (WGN) from the measuring instrument is the most interference noise embedded together in the arcing signals captured [4] [11].…”

Section: Introductionmentioning

confidence: 99%

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Arcing fault diagnosis using enhanced cross-correlation technique

Amirruddin

Adzman

Affendi

et al. 2022

J. Phys.: Conf. Ser.

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This study explored the potential use of cross-correlation as a technique for detecting arcing faults in a power system distribution network. The cross-correlation technique was employed to investigate the effect of each antenna placement as a detection device, time difference of arrival (TDOA), time delay, and correlation magnitude of arcing signals detected during on-line arcing fault measurement. The arcing fault was detected using four antennas that had been set up around the arc source point in a high voltage (HV) laboratory. The measurements were taken using a digital oscilloscope. For precise results, the Discrete Wavelet Transform (DWT) denoising technique combined with cross-correlation (CC) technique were applied using MATLAB software to identify the arcing signals detected in order to diagnose the differentiation between noisy and real arcing fault signals. Further assessment was carried out by performing a cross-correlation technique on the real arcing signals obtained to find the similarities and arrival time’s delay between single arcing signals’ placement. The outcome shows that all measurements including the time difference of arrival (TDOA), correlation magnitude, time delay, and antennas’ placement towards the arcing source point are valuable in determining the arcing signals detected precisely.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arcing fault diagnosis using enhanced cross-correlation technique

Amirruddin

Adzman

Affendi

et al. 2022

J. Phys.: Conf. Ser.

Self Cite

View full text Add to dashboard Cite

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Research on Low-Voltage AC Series Arc-Fault Detection Method Based on Electromagnetic Radiation Characteristics

Zhang

Suo

et al. 2022

Energies

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Arc fault is an important cause of electrical fire. At present, the arc-fault detection method based on current and voltage is vulnerable to the influence of a nonlinear load and switching operation in the line, resulting in misjudgment and omission. Therefore, an arc-fault detection method based on the characteristics of electromagnetic radiation is proposed. A low-voltage AC series arc-fault simulation platform is built, and a simple annular antenna is designed to receive an electromagnetic radiation signal. It is proved by experiments that electromagnetic radiation signals have similar characteristic frequencies (13.6–14.2 MHz) under different currents, loads, arc positions and arc occurrence times. At the same time, the electromagnetic radiation signal of a low-voltage AC series arc and normal switching operations are compared. The pulse oscillation time of the radiation signals of the operating arc (2 μs) is far shorter than that of the faulty arc (4 μs), and the characteristic frequency of the radiation signal generated by the switching operation (9.35 MHz) is significantly lower than that of the series arc radiation signal (14 MHz). Compared with the existing methods, this method does not need to consider the influence of current, nonlinear load and other factors in the line, and can accurately distinguish the operating arc and faulty arc.

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Arcing fault diagnosis using first peak arrival of EM radiation signal

Cited by 2 publications

References 9 publications

Arcing fault diagnosis using enhanced cross-correlation technique

Arcing fault diagnosis using enhanced cross-correlation technique

Research on Low-Voltage AC Series Arc-Fault Detection Method Based on Electromagnetic Radiation Characteristics

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