Arc fault detection and identification via non-intrusive current disaggregation

Luan, Wenpeng; Lin, Jian-Li; Liu, Bo; Zhao, Baohua

doi:10.1016/j.epsr.2022.108113

Cited by 17 publications

(12 citation statements)

References 34 publications

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“…To evaluate the accuracy of the proposed arc detection method, a measure based on mean absolute error (MAE), used in the most recent publications, was adopted [16]:…”

Section: Resultsmentioning

confidence: 99%

“…The literature study verified the usefulness of the presented series arc indicators, while modifications were proposed for some of them. Time features exploited in existing methods are Zero Current Period (ZCP) [15][16][17][18], the ratio of the current rate of change to the RMS value (CRC) [18], Maximum Split Difference (MSD) [15], and measures related to Euclidean distance (E, MED -Maximum Euclidean Distance) between adjacent cycles [15][16][17]. Arc detection accuracy of 99.1% and load identification of 99.3% were achieved using PCA and SVM in [15], but it considers only the case of individually operating devices.…”

Section: Related Work In the Literaturementioning

confidence: 99%

“…Arc detection accuracy of 99.1% and load identification of 99.3% were achieved using PCA and SVM in [15], but it considers only the case of individually operating devices. In [16], where up to 4 appliances operate simultaneously, arc detection accuracy was 94.86%. Load identification and line selection accuracy was 90.91%, with consideration that the harmonic disaggregation results of total current can be obtained by superposition of the harmonic disaggregation results of each appliance current either with or without arc fault.…”

Section: Related Work In the Literaturementioning

confidence: 99%

“…Using an array notation of signals, a method of calculating the following signal features for every signal period was prepared. The feature 𝐸 (2) is the sum of differences between adjacent periods from the array 𝐼 [16]. The third feature is the modified CRC feature proposed in [18].…”

Section: B Data Processing and Feature Extractionmentioning

confidence: 99%

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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

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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%.

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“…To evaluate the accuracy of the proposed arc detection method, a measure based on mean absolute error (MAE), used in the most recent publications, was adopted [16]:…”

Section: Resultsmentioning

confidence: 99%

Section: Related Work In the Literaturementioning

confidence: 99%

Section: Related Work In the Literaturementioning

confidence: 99%

Section: B Data Processing and Feature Extractionmentioning

confidence: 99%

See 2 more Smart Citations

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

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“…However, under the condition of nonlinear load, it can also appear "zero rest" when the arc fault does not occur, which easily causes misjudgment. The research work presented in [11][12][13][14] utilize signal processing methods to detect the singularity of fault currents, which has high detection sensitivity. However, the harmonic content of distribution network current is high, and the current waveform itself has certain singularity, which is also prone to misjudgment.…”

mentioning

confidence: 99%

Detection of Series Arcs in Low-Voltage AC Power Lines Using Load Side Voltage Sag and Waveform Similarity Comparison

Wang,

Lin,

Wei

et al. 2024

IEEE Access

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Aim at problems that the similarity between series arc fault current waveform and nonlinear load currents waveform causes arc fault identification methods based on arc current to be prone to misjudgment. This paper established a unified fault criterion based on the conclusion that different types of loads that exhibit similar magnitudes of load side voltage sag during arc faults. Combined the voltage sag criterion with waveform criteria, this paper proposed a novel arc fault detection method. The method takes the load side voltage sag value exceeding the threshold as the detection start condition. After detection starts, the method identifies faults by calculating the degree of load side voltage waveform distortion using Hausdorff distance algorithm. Simulations were conducted on five different loads, setting the voltage sag threshold to 10.8V and Hausdorff distance threshold to 39.26. Test results show that the fault identification accuracy of the proposed method is 100% under the above thresholds.INDEX TERMS Series arc fault, arc voltage, voltage sag, Hausdorff distance.

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