Immediate detection and management of arc faults in the electric circuits of road tunnels

Road tunnels are high-risk environments, especially in the event of a fire. These environments are at a high technological level, as they are equipped, as required by the regulations, with complex systems to prevent accidents and reduce the severity of the consequences. The safety of a tunnel depends on the reliability of equipments and their electrical supply circuits. Detection of faults in electrical circuits is essential for the correct operation of all tunnel systems and for maintenance management.

This study deals with a system for detecting arc faults with AFDD (Arc Fault Detection Device), optimized for electrical circuits in road tunnels. Arc faults are a particular type of electrical faults that can occur in parallel, towards earth and in series to a circuit. These faults easily lead to the functional loss of the circuit and often cause a fire. Arc faults are difficult to detect, particularly when they are in series to the circuits. Serial arc faults are not perceived by the common protection devices (fuse, Miniature Circuit Breaker and Residual Current Device), so to face them we usually use programmed maintenance and sensors that detect the effects of the arc, such as temperature sensors, flame and smoke detectors. Today with the AFDD it is possible to detect the waveform generated by an arc fault in an electric circuit, but these devices are currently built for domestic or similar environments and can be critical if applied to particular circuits. To overcome the critical issues related to the use of AFDD in tunnels, this study proposes a modified device and an innovative architecture of the detection system to use AFDD in road tunnels. This new system can give an immediate detection of the arc fault and a high availability of the protected circuits. In general, the proposed solution can be a valid help against the series fault, so a good system to increase the safety of the road infrastructure.

Road tunnels are high-risk environments, especially in the event of a fire. These environments are at a high technological level, as they are equipped, as required by the regulations, with complex systems to prevent accidents and reduce the severity of the consequences. The safety of a tunnel depends on the reliability of equipments and their electrical supply circuits. Detection of faults in electrical circuits is essential for the correct operation of all tunnel systems and for maintenance management.

This study deals with a system for detecting arc faults with AFDD (Arc Fault Detection Device), optimized for electrical circuits in road tunnels. Arc faults are a particular type of electrical faults that can occur in parallel, towards earth and in series to a circuit. These faults easily lead to the functional loss of the circuit and often cause a fire. Arc faults are difficult to detect, particularly when they are in series to the circuits. Serial arc faults are not perceived by the common protection devices (fuse, Miniature Circuit Breaker and Residual Current Device), so to face them we usually use programmed maintenance and sensors that detect the effects of the arc, such as temperature sensors, flame and smoke detectors. Today with the AFDD it is possible to detect the waveform generated by an arc fault in an electric circuit, but these devices are currently built for domestic or similar environments and can be critical if applied to particular circuits. To overcome the critical issues related to the use of AFDD in tunnels, this study proposes a modified device and an innovative architecture of the detection system to use AFDD in road tunnels. This new system can give an immediate detection of the arc fault and a high availability of the protected circuits. In general, the proposed solution can be a valid help against the series fault, so a good system to increase the safety of the road infrastructure.


ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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