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

Abstract

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

Binary classification model based on machine learning algorithm for the DC serial arc detection in electric vehicle battery system

“…When the DC serial arc is generated, there are usually some special physical phenomenon such as heat, light, current jumping and electromagnetic interference (EMI). These physical phenomena can usually be applied for the detection of DC serial arc [4][5][6][7][8][9]. In the literature [3], when the arc occurs, the current in the line suddenly jumps, and a series of EMI signals corresponding to the current pulse could be observed from the detection instrument.…”

“…Therefore, the managerial unit could adopt appropriate measures to protect the system. Moreover, Faifer et al [7] detected the occurrence of DC arc faults by analysing the current and the energy related to the arc in the circuit. In photovoltaic micro grids, differential current analysis was also used to detect DC arc faults in the circuit [8,9].…”

Diagnosis of Series DC Arc Faults—A Machine Learning Approach

“…At dc levels, the increased probability of a sustained arcing event means they present a significant fire hazard. Their presence has been known to affect the secure and reliable distribution of power in photovoltaic [8], aircraft [9], and shipboard [10] systems.…”