A series DC arc fault detection method and hardware implementation

Yao, Xin; Herrera, Luis; Wang, Jin

doi:10.1109/apec.2013.6520638

Cited by 8 publications

(9 citation statements)

References 7 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A method based on both time and time-frequency domain features for the application in DC microgrid networks is presented by Yao et al in [40]. Implemented through a DSP board, the detection algorithm calculates the maximum and minimum values and the corresponding difference, from the time domain arc current data through a specified 25ms window.…”

Section: Time-frequency Domainmentioning

confidence: 99%

“…Broadband noise [27] Aircraft EPS Series/Parallel AC Harmonic ratios [28] Power Substation Parallel AC Mahalanobis [29] Circuit Interrupters Series AC General arc FDI [11] Aircraft EPS Series/Parallel AC/DC Sliding DFT window [33] Spacecraft EPS Series/Parallel AC/DC Frequency component [34] Aircraft EPS Series DC ANN [35] Spacecraft EPS Series DC BSVM [36] PV Series DC DTW [37] AC Distribution Series DC Time-Frequency STFT [38] PV Series DC WT [39] Circuit Interrupters Series/Parallel DC DSP [40] PV Series DC HMM [41] MEA EPS Series DC…”

Section: Frequencymentioning

confidence: 99%

See 1 more Smart Citation

DC Arc Fault Detection Methods in MEA Distribution Systems

Thomas¹,

Telford²,

Rakhra³

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

Direct current (DC) for primary power distribution is a promising solution that is being explored by aircraft system integrators for MEA applications to enable the paralleling of non-synchronized engine off-take generators, and to enable the reduction of energy conversion stages required to supply electronically actuated loads. However, a significant challenge in the use of DC systems is the reliable detection of arc faults. Arcing presents a significant fire risk to aircraft and their presence can result in critical system damage and potentially fatal conditions. Series arc faults in DC systems are particularly challenging to detect as the associated reduction in system current eliminates the use of conventional overcurrent and current differential methods for fault detection. This paper provides an overview of series arc faults in DC systems and presents both simulation and hardware results to illustrate key trends, characteristics and discriminating features. It also presents a comprehensive review of arc fault detection and diagnosis techniques that have been proposed for a wide range of aerospace and other applications. The paper concludes with a discussion on the unique challenges and opportunities for the application of both deterministic and probabilistic methods in MEA systems.

show abstract

Section: Time-frequency Domainmentioning

confidence: 99%

Section: Frequencymentioning

confidence: 99%

DC Arc Fault Detection Methods in MEA Distribution Systems

Thomas¹,

Telford²,

Rakhra³

et al. 2018

SAE Technical Paper Series

View full text Add to dashboard Cite

show abstract

“…Various statistical features could be applied in the identification of DC arc faults, including entropy, RMS value, standard deviation, mean, and the highest value of the input signal. Basing on time-domain analysis, this approach is present in [60][61][62][63][64][65][66][67][68]. Although the rate of changes in the loop current in the time domain was suggested in [63] to indicate the arc fault event, it would receive the impact of random spike disturbance.…”

Section: Statistics-based Methodsmentioning

confidence: 99%

“…This was followed by the calculation of the distance of the I-V characteristic curve between each PV module and the centre of the administration of PV module by the MCD estimator, which could be applied for identification. A Finite Impulse Response (FIR) estimator was applied in [67] to measure the difference of the input voltage signal under the 50 kHz sampling frequency. Initially, the input signal would flow through a band-pass filter with 1 kHz and 7.5 kHz cut-off frequency before being placed into the estimator.…”

Section: Statistics-based Methodsmentioning

confidence: 99%

Models, Detection Methods, and Challenges in Dc Arc Fault: A Review

et al. 2021

View full text Add to dashboard Cite

The power generation of solar photovoltaic (PV) technology is being implemented in every nation worldwide due to its environmentally clean characteristics. Therefore, PV technology is signiﬁcantly growing in the present applications and usage of PV power systems. Despite the strength of the PV arrays in power systems, the arrays remain susceptible to certain faults. An effective supply requires economic returns, the security of the equipment and humans, precise fault identiﬁcation, diagnosis, and interruption tools. Meanwhile, the faults in unidentiﬁed arc lead to serious ﬁre hazards to commercial, residential, and utility-scale PV systems. To ensure secure and dependable distribution of electricity, the detection of such hazards is crucial in the early phases of the distribution. In this paper, a detailed review of modern approaches for the identiﬁcation of DC arc faults in PV is presented. In addition, a thorough comparison is performed between various DC arc-fault models, characteristics, and approaches used for the identiﬁcation of the faults.

show abstract

“…The multiple components not only have their own characteristics but also interact with each other, affecting the system operations. Thus, detecting and locating an arc fault is more challenging in dc systems than in PV systems [5].…”

mentioning

confidence: 99%

A new DC arc fault detection method using DC system component modeling and analysis in low frequency range

Seo

Kim

Lee

et al. 2015

2015 IEEE Applied Power Electronics Conference and Exposition (APEC)

View full text Add to dashboard Cite

In this paper, system component and arc modeling are presented, along with low frequency range analysis for dc arc fault detection. With an arc fault model consisting of a voltage source and variable resistor, the system during arcing can be described as an equivalent circuit model, which gives motivation for the proposed arc detection method. The presented model represents the low frequency characteristics of the main system components and arc in a dc system. Their influence on the line current can be well described and, as a result, it can be utilized for arc detection. The proposed method can distinguish normal operation from arcing and, as a result, dramatically reduces the possibility of false-positives. Experimental results verify the model validity and performance of the method.

show abstract

A series DC arc fault detection method and hardware implementation

Cited by 8 publications

References 7 publications

DC Arc Fault Detection Methods in MEA Distribution Systems

DC Arc Fault Detection Methods in MEA Distribution Systems

Models, Detection Methods, and Challenges in Dc Arc Fault: A Review

A new DC arc fault detection method using DC system component modeling and analysis in low frequency range

Contact Info

Product

Resources

About