Smart Detection Of AC Series Arc Fault On Home Voltage Line Based On Fast Fourier Transform And Artificial Neural Network

Abdulrachman, Muhammad Alwi; Prasetyono, Eka; Anggriawan, Dimas Okky; Tjahjono, Anang

doi:10.1109/elecsym.2019.8901671

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…However, protections based on time-domain signatures require acquiring data, training the protection and defining threshold values, thus limiting their widespread use [70], [75]. Mathematical methods such as artificial neural networks [74], [79], [80], Kalman filters [81] or fuzzy logic [72], [82] have been applied to identify the specific patterns of the arcs. Another possibility is to apply frequency-domain signatures, such as the harmonic content of the fault current [83], but fault current harmonic frequencies can be masked by those triggered by other faults [84].…”

Section: B State Of the Art Methods To Detect Arcing Faultsmentioning

confidence: 99%

“…Harmonic-based protections often compare the extracted harmonics by means of the fast Fourier transform (FFT) [85] using pre-established threshold values, thus limiting once again its applicability. In [74] the fast Fourier transform (FFT) was combined with an artificial neural network (ANN) to detect series arc faults. Harmonic extraction based on the fast Fourier transform can be improved by applying wavelet decomposition [12] to enhance identification accuracy [86].…”

Section: B State Of the Art Methods To Detect Arcing Faultsmentioning

confidence: 99%

“…Arcs are commonly detected and isolated by analyzing the line current waveform and its broadband RF content [73]. The shape of the current waveform and its harmonic content during arc fault occurrence are unique and complex, thus requiring specific techniques for its detection [74].…”

Section: A Effects Of Arcing Faultsmentioning

confidence: 99%

See 2 more Smart Citations

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Riba¹,

Moreno-Eguilaz²,

Ortega³

2022

IEEE Trans. Instrum. Meas.

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Arc faults are serious discharges, damaging insulation systems and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of more electric aircrafts (MEA), future airliners will require more electrical power to enhance fuel economy, save weight and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with the increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This paper reviews the state-of-theart of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers and international regulations.

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Section: B State Of the Art Methods To Detect Arcing Faultsmentioning

confidence: 99%

Section: B State Of the Art Methods To Detect Arcing Faultsmentioning

confidence: 99%

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Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Riba¹,

Moreno-Eguilaz²,

Ortega³

2022

IEEE Trans. Instrum. Meas.

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“…In ref. [14], FFT is used to analyze the current signal, and the unique spectral characteristics of fault are utilized to realize fault detection. In ref.…”

mentioning

confidence: 99%

Series arc fault detection method based on Wavelet energy spectrum entropy

Fu,

Wang,

et al. 2023

Preprint

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The existing series arc fault detection methods mostly construct the detection criterion by extracting the fault characteristics of current. Due to the diversity of load currents, it is difficult for current detection methods to construct detection criteria with a single characteristic. In contrast, the normal voltage on the load side is not sensitive to the types of loads, thus the arc fault information has a more obvious characterization on the load side voltage. In view of this reason, based on the analysis of basic time-frequency domain characteristics of arc faults, a method for arc fault detection is proposed based on wavelet energy spectrum entropy of the voltage on the load side whose purpose is to overcome the difficulty of selecting fault characteristic frequency band under different loads, and the optimal selection of wavelet decomposition layers and wavelet basis functions are also achieved through analysis and experiments. The experiment and comparison of algorithms show that the fault detection accuracy of the proposed method is more than 98% under different loads. The anti-disturbance ability of the method for load switching is verified by load switching experiment, which verifies the effectiveness of the method.

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“…Topologi ANN BPMengenai penghitungan metode jaringan saraf tiruan ANN dari hasil pelatihan, metode ANN yang melibatkan input layer, hidden layer, dan output yang berurutan, dimulai dari normalisasi input hingga denormalisasi output. Secara umum, persamaan metode ANN tersebut dapat dirumuskan sebagai berikut[14].…”

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Implementation of Fast Fourier Transform and Artificial Neural Network in Series Arc Fault Identification and Protection System on DC Bus Microgrid

Anggriawan,

Sunarno,

Sunarno

et al. 2023

JTT (Jurnal Teknologi Terpadu)

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A microgrid is a cluster of electrical sources and loads that are interconnected and synchronized. Microgrid operation is typically divided into two modes, isolated or connected to the grid with a single or standalone control system. In this context, it can enhance the reliability and quality of electricity supply for connected customers. When using a microgrid system, it is important to consider the risk of series arc faults. Series arc faults are sudden bursts of flames resulting from ionization of gas between two electrode gaps. These faults can occur due to manufacturing defects, installation Errors, aging, or corrosion on conductor rods, leading to imperfect connections. Detecting series arc faults in DC microgrid system operations can be challenging using standard protective devices. Failure in the protection system can pose risks of fire, electrical shock hazards, and power loss in the DC microgrid.Therefore, a device has been designed to detect series arc faults by utilizing the fast Fourier transform method and artificial neural network, which function to analyze DC signal and make decisions when faults occur by examining the average sum of current frequency values during normal and fault conditions. In this study, the average sum of current frequency values during normal conditions was found to range from 0.35437 to 0.36906 A, while during fault conditions, it ranged from 0.21450 to 0.22793 A, with an average protection identification time of 1087 ms and an ANN output accuracy of 99.98%.

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Smart Detection Of AC Series Arc Fault On Home Voltage Line Based On Fast Fourier Transform And Artificial Neural Network

Cited by 7 publications

References 9 publications

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Series arc fault detection method based on Wavelet energy spectrum entropy

Implementation of Fast Fourier Transform and Artificial Neural Network in Series Arc Fault Identification and Protection System on DC Bus Microgrid

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