An Arc Fault Detection Method Based on Current Amplitude Spectrum and Sparse Representation

Qu, Na; Wang, Jianhui; Liu, Jinhai

doi:10.1109/tim.2018.2880939

Cited by 51 publications

(14 citation statements)

References 12 publications

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“…From 0 to 1 ms, the circuit was working normally without the arc. The average current signal I mean was 2.10 A, the average voltage signal V mean was 63.73 V and the average optical signal U mean was close to 0 V. Prior to the series arc, the ripple peak value of the current signal reached 1.43 A, and the ripple peak value of the voltage signal reached 219.07 V, when the optical signal was −7.34 V. Corresponding to the response of the respective sensors, the threshold value was set as 10 times the difference between the ripple peak value and the average value without the arc, then the average value was also considered, as shown in (6) Thr = R max − R mean /20 + R mean (6) As a result, the threshold of the current signal was 2.06 A, the threshold of voltage signal was 79.26 V and that of the optical signal was 1.12 V. The arc fault was detected by the optical signal at t = 0.938 ms prior to the current and voltage signal at t = 0.976 ms.…”

Section: Arc Optical Detection and Discussionmentioning

confidence: 99%

“…With great advantages of large capacity, few distribution cables and high reliability, a MEA high voltage direct current (HVDC) system is recognised as the ideal power supply system of MEA [2,3]. However, the higher voltage level (±270 V) [4] increases the risk of arc faults, easily leading to overheating accidents, even as high as thousands of Celsius [5,6], and the high temperature can damage the structure of the aircraft within a short period of time. Different from the well-known parallel arc fault, the series arc phenomenon occurs on the conditions of crushed cables, poor connections or pin-socket connections, making the arcing current less than the load current and it is difficult to be detected [7].…”

Section: Introductionmentioning

confidence: 99%

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Detection of DC series arc in more electric aircraft power system based on optical spectrometry

et al. 2020

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Series arcing faults have been recognised as a hazardous phenomenon in the power system of more electric aircraft (MEA), especially with the increasing voltage level and power supply. Instead of the electrical method, the optical method can be used as an approach to detect arc fault with the advantages of fast response and immunity to electromagnetic interference. It is the basis of optical arc detection to determine the distribution spectrum range of direct current (DC) arc fault. Aiming at series arc optical detection in the MEA DC system, a DC series arc test platform was established to measure the series arc spectrum. The spectrum was disassembled though empirical mode decomposition to extract characteristic wavelengths of arc light and denoised by means of wavelet decomposition and reconstruction (wavelet transformation) to reduce equipment noise. To resolve the baseline drift, the baseline of the arc spectrum measured was calculated and corrected based on Savitzky−Golay. The experimental results indicate that there are several series arc characteristic spectra of the MEA DC system, including 309.3 and 324.5 nm in the ultraviolet range. Moreover, the arc spectrum with different metals, copper and aluminium, were contrasted, indicating the arc spectral behaviour is related to the melting/boiling point and chemical properties of the anode material. The measurement of the series arc spectrum makes it more effective to apply optical detection in the MEA discharging faults diagnosis.

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Section: Arc Optical Detection and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of DC series arc in more electric aircraft power system based on optical spectrometry

et al. 2020

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“…Reference [16] has analyzed low-frequency harmonic components based on CZT, from 0Hz to 500Hz, and difference of current signals. Reference [17] proposes to detect SAFs in some loads by current amplitude spectrum and norm-based sparse representation. Final tests show L3/4 norm is the best parameter and its detection accuracy can reach a high level.…”

Section: Introductionmentioning

confidence: 99%

A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR

Jiang

Bao

Hong

et al. 2021

IEEE Trans. Instrum. Meas.

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This paper presents a new method for effective detection of AC series arc fault (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and non-arcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the non-arcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting arc faults compared with existing methods.

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“…Los distintos métodos están basados en el análisis frecuencial, como es el análisis del rango de frecuencias hasta 20 kHz, las variaciones del 3er y 5to armónico (Artale, 2017), las variaciones a alta frecuencia superior a 20MHz (Bao, 2019) y evaluación de armónicos (Qu, 2018) pares e impares. De la misma forma otros métodos están basados en el análisis en el dominio temporal (Yanq, 2020) como la variación armónica, la autocorrelación, la transformada de wavelet (Qi, P, 2017), redes neuronales (Le, 2020), entre otros.…”

Section: Introductionunclassified

Detección de fallas de arco eléctrico en redes eléctricas domesticas AC basado en el análisis de la tensión y corriente

2021

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La protección de las instalaciones eléctricas basadas en disyuntores electromagnéticos permite la detección de fallas de tipo cortocircuito y sobre corriente, sin embargo, se ha observado la presencia de fallas de tipo arco eléctrico, los cuales han sido los iniciadores de incendios en viviendas de Estados Unidos y Europa, debido a que las fallas de tipo arco eléctrico serie pueden generar altas temperaturas. Este articulo presenta un método de detección de fallas de arco eléctrico serie basado en el análisis de la forma de onda y la rapidez de cambio del flujo de corriente al momento de la producción de la descarga entre dos electrodos. Los resultados presentados muestran la posibilidad del uso de este método para la detección de fallas eléctricas y fallas de tipo arco eléctrico serie en las instalaciones eléctricas.

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An Arc Fault Detection Method Based on Current Amplitude Spectrum and Sparse Representation

Cited by 51 publications

References 12 publications

Detection of DC series arc in more electric aircraft power system based on optical spectrometry

Detection of DC series arc in more electric aircraft power system based on optical spectrometry

A Coupling Method for Identifying Arc Faults Based on Short-Observation-Window SVDR

Detección de fallas de arco eléctrico en redes eléctricas domesticas AC basado en el análisis de la tensión y corriente

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