An evaluation of diagnostic techniques relevant to arcing fault current interrupters for direct current power systems in future aircraft

Spyker, R.L.; Schweickart, D.L.; Horwath, J.; Walko, L.C.; Grosjean, Dennis

doi:10.1109/eeic.2005.1566277

Cited by 25 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A previous study found [29] that the frequency range of the current arc is 2-100 kHz, and it is not smooth. Reference [15] found that the amplitude fluctuation of 2.4-39 kHz is more obvious by analyzing the spectrum of different levels after wavelet decomposition.…”

Section: Frequency Band Distribution Of the Wavelet Transformmentioning

confidence: 92%

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Han

Huang

et al. 2020

Electronics

View full text Add to dashboard Cite

The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

show abstract

Section: Frequency Band Distribution Of the Wavelet Transformmentioning

confidence: 92%

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Han

Huang

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“… During the arc the rate of variation of the current is considerably higher than that in normal operating conditions [3] [13].…”

Section: The Series Arc Faultmentioning

confidence: 99%

A method for the detection of series arc faults in DC aircraft power networks

Faifer

Ottoboni

Rossi

et al. 2013

2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

View full text Add to dashboard Cite

On up-to-date aircrafts the electric power system is a very complex and crucial system, since great part of the equipment requires power supply. Its protection is a critical task as a fault may be catastrophic if it is not quickly detected and isolated. Thermal magnetic breakers or fuses are commonly employed to provide overload and short circuit protection. Unfortunately they are not effective in detecting other dangerous faults such as electrical arcs. In the last decades many systems able to detect series and parallel arcs have been developed. Most of them are complex devices that involve custom hardware and high computational burden. In this paper the authors propose a method for the identification of the series arcs in DC power systems which is based on a simple analysis of the current signal and on the evaluation of the energy related to the arc itself. In the paper the developed technique and the related experimental results are presented

show abstract

“…PD can be assigned several different PD categories such as Surface Discharge, and Internal Discharge [ 4 ]. An arcing fault is also an electrical discharge through an insulating medium [ 5 ]. An arc occurs when electric current is transmitted through a gaseous or liquid environment.…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation

Mitiche

Morison

Nesbitt

et al. 2018

Entropy

View full text Add to dashboard Cite

This work exploits four entropy measures known as Sample, Permutation, Weighted Permutation, and Dispersion Entropy to extract relevant information from Electromagnetic Interference (EMI) discharge signals that are useful in fault diagnosis of High-Voltage (HV) equipment. Multi-class classification algorithms are used to classify or distinguish between various discharge sources such as Partial Discharges (PD), Exciter, Arcing, micro Sparking and Random Noise. The signals were measured and recorded on different sites followed by EMI expert's data analysis in order to identify and label the discharge source type contained within the signal. The classification was performed both within each site and across all sites. The system performs well for both cases with extremely high classification accuracy within site. This work demonstrates the ability to extract relevant entropy-based features from EMI discharge sources from time-resolved signals requiring minimal computation making the system ideal for a potential application to online condition monitoring based on EMI.

show abstract

An evaluation of diagnostic techniques relevant to arcing fault current interrupters for direct current power systems in future aircraft

Cited by 25 publications

References 3 publications

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

A method for the detection of series arc faults in DC aircraft power networks

Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation

Contact Info

Product

Resources

About