Learning approach based DC arc fault location classification in DC microgrids

Pan, Prateem; Mandal, Rajib Kumar

doi:10.1016/j.epsr.2022.107874

Cited by 15 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper is focused on the arc faults phenomenon in low-voltage AC networks. It should be noted that there also is a wide research area for DC series arc detection, especially in photovoltaics systems [7][8][9][10]. DC signals are not periodic, so arc fault may not be easily detected via recognizable amplitude or frequency signatures for pattern recognition techniques.…”

Section: Related Work In the Literaturementioning

confidence: 99%

“…DC signals are not periodic, so arc fault may not be easily detected via recognizable amplitude or frequency signatures for pattern recognition techniques. Arc detection and location systems for DC networks often exploit spectral analysis with Short Term Fourier Transform [7], Wavelet Transform [8], and Discrete Wavelet Transform [9]. Also use of Adaptive Local Mean Decomposition was proposed in [10].…”

Section: Related Work In the Literaturementioning

confidence: 99%

See 1 more Smart Citation

Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

Dowalla¹,

Bilski²,

Kowalik³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

View full text Add to dashboard Cite

The series arc faults are a common cause of household fires. Low fault current amplitude is the reason for the difficulties in implementing effective arc detection systems. The paper presents a novel arc detection and line selection method. It can be easily used in the low-voltage Alternate Current (AC) network to enhance the functionality of the Non-Intrusive Load Monitoring (NILM) system with arc detection for the whole household. Unlike existing methods, the proposed approach exploits not only current signal but also voltage signal time domain analysis. In the case of arc fault detection, line selection is based on the mean values of changes in the consecutive current signal periods during the arc and comparing them with current waveforms for each appliance in non-arc conditions. Tests have been conducted with up to 6 devices operating simultaneously in the same circuit. Single period arc detection accuracy was 98.47%, with recall at 97.5% and F-score of 0.983. The arc detection accuracy in terms of the IEC 62606:2013 standard was 99.33%, Fscore of 99.33. Line selection accuracy was 91.57%.

show abstract

Section: Related Work In the Literaturementioning

confidence: 99%

Section: Related Work In the Literaturementioning

confidence: 99%

Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

Dowalla¹,

Bilski²,

Kowalik³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

View full text Add to dashboard Cite

show abstract

“…They are contribution of all different components to supply the fault current. In this equation, i pv is PV unit contribution in Figure 2 to supply the fault that is given as Equation (25):…”

Section: Second Layer Agent Operation Of the Central Processormentioning

confidence: 99%

“…Besides, the fault current in an AC grid has a zero-crossing point due to its alternating nature. But in a DC grid, the fault current will never have a zero point due to the direct nature of the fault current, and breaking the fault current is not simply an AC fault current [23][24][25]. Also, a phase to ground high impedance fault is difficult to detect because the fault current is small and might not variate the current path at fault beginning [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bi‐level multi‐agent system (MAS) to protect DC microgrids (DCMGs) in free renewable sources and uncertainties penetration

Bamshad,

Ghaffarzadeh

2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

DC microgrids (DCMGs) have significant advantages, but their protection can be a challenge for DCMGs expansion expressly in distributed generation sources’ (DGS) tendance. This paper proposes a quick, and precise strategy to perceive and trip any fault in DCMGs in the free tendance of DGS. A bi‐level multi‐agent system (MAS) is proposed to protect the DCMG and DGs. The MAS first level perceives the fault in DCMG via a discrete wavelet transform (DWT). The multiply of two harvest levels’ summation of DWT is exerted to detect and distinguish DC lines faults. The MAS second level detects the fault in DGS and their switchers to the point of common connection (PCC) via a differential pole (DP) controller. A four bus DCMG containing solar PV and wind sources is implemented, and the results demonstrate that the proposed plan can recognize and trip the fault by 100% of reliability.

show abstract

An ANFIS-based approach to locate arc fault in smart grid using phasor measurement units (PMU)

Gang

2023

Multiscale and Multidiscip. Model. Exp. and Des.

View full text Add to dashboard Cite

Learning approach based DC arc fault location classification in DC microgrids

Cited by 15 publications

References 43 publications

Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

Series arc fault detection and line selection based on Non-Intrusive Load Monitoring Method

Bi‐level multi‐agent system (MAS) to protect DC microgrids (DCMGs) in free renewable sources and uncertainties penetration

An ANFIS-based approach to locate arc fault in smart grid using phasor measurement units (PMU)

Contact Info

Product

Resources

About