A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring

Dowalla, Krzysztof; Bilski, Piotr; Łukaszewski, Robert; Wójcik, Augustyn; Kowalik, Ryszard

doi:10.3390/en16010171

Cited by 5 publications

(3 citation statements)

References 28 publications

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“…Wang et al [13] first determined the load category according to the fundamental frequency component of the current signal, and then took the specific time-domain and frequency-domain indicators as the input, and finally adopted different FCNNs to identify the SAF occurred in different types of load circuit. Dowalla et al [14] presented a SAF detection and fault location method by analyzing both current and voltage signals. The random forest classifier and k-nearest neighbor algorithm were adopted respectively to realize the fault detection and fault location function.…”

Section: Introductionmentioning

confidence: 99%

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

Tang,

Wang,

Tian

et al. 2024

IEEE Access

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The fast and accurate series arc fault (SAF) identification method and its hardware implementation are the key to the development of arc fault circuit interrupter (AFCI) or arc fault detection device (AFDD). The SAF experiments under household multi-branch circuit conditions were conducted. And a novel SAF identification model based on lightweight one-dimensional (1-D) convolutional neural network was proposed. First, the main-circuit current signal was used as the input of the model. The 1-D convolutional layers and 1-D maximum pooling layers of the model were used to extract the features of the current signal. The fully connected neural network (FCNN) was used to identify whether or not there is a SAF in the circuit and determine the branch-circuit where the fault is located. Second, the second to fourth standard convolutional layers of the model were improved by using depthwise separable convolution, and the batch normalization layers were added to the model, so as to realize the optimal design of the model. Finally, the model was deployed to an embedded device and its performance was tested. When the sampling frequency is higher than 5 kHz, the accuracy of fault identification and fault line selection of the model in the embedded device is higher than 98.05% and 99.11%, respectively. The average runtime of single identification is 5.26ms. It meets the technical requirements of household AFCI or AFDD.

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Section: Introductionmentioning

confidence: 99%

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

Tang,

Wang,

Tian

et al. 2024

IEEE Access

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“…Furthermore, the impact of cable insulator degradation [24][25][26] on the critical parameters, maximum voltage values, maximum current values, and average and maximum power of the protection module for the components remains unknown, but this phenomenon is present in the railway environment due to the need to ensure the long life of the equipment and therefore it needs to be examined. The input data representing the model (Figure 1) and the parameters of the degraded cable are described in detail in ref.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Cable Aging on Surge Arresters Designed by Genetic Algorithm

Zieliński,

Grzechca

2023

Applied Sciences

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The degradation of communication cable insulators reduces the availability of the train control system and is caused by the degrading of data transmission line parameters. Due to extremely varying environmental conditions in the railway industry, the effect of data transmission line degradation on the critical parameters of the surge arrester that is used in wayside equipment is not known. The surge arrester module is very important in the railway industry, as it enables the protection of sensitive parts of wayside equipment, especially communication interfaces existing in wheel detectors, evaluators, signals, etc. These are critical to the safe and efficient management of railway infrastructure. The process of selecting a surge arrester component for a two-level voltage input to enable the examination of the degradation impact of a cable insulator used in rail signaling equipment on the performance of the surge arrester is presented in this paper based on the standards, as well as practical experience. To achieve this objective, an innovative design of the goal function that can be adapted to the new requirements of various industry sectors is proposed. The use of a two-stage solution verification is a unique feature of this method. The results indicate that the proper selection of the arrester components ensures the effect of degradation of the communication cable has an insignificant influence on the power dissipation of the surge exposures.

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“…A current waveform database of normal and arc fault electric appliances is constructed, and the aggregated current at the power supply entrance is decomposed in real time to detect and locate the arc faults. Reference [35] proposes a two-step method for series arc fault detection and localization, based on the MVC 50 feature vector, but it is difficult to collect the arc voltage signal using this method, and it is prone to wrong localization for the same types of electric appliances.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

Jiang

Liu

Yang³

et al. 2023

Energies

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In recent years, electrical fires caused by arc faults have been increasing, seriously affecting the safety of people’s lives and property. Considering the complex arc fault characteristics of actual low-voltage users, the non-intrusive arc fault detection and localization method is studied. First, the characteristics of arc current waveforms are analyzed, and event detection based on the Mann–Kendall Test is performed for the difference between the current waveforms of two adjacent cycles, rather than using the current waveforms directly. Then, the current waveforms of the two segments are calculated via subtraction to obtain the current waveform of the electric appliances causing the event. A current feature parameter database of the normal and arc currents is constructed via harmonic analysis, and a multi-appliance current decomposition model considering the sparse operation characteristics of appliances is established; thus, the arc localization problem is transformed into an optimization problem. Finally, a genetic algorithm is used to optimize the differential current decomposition results, and then, locate the arc fault. A household arc fault simulation experiment is carried out for the common electric appliances of actual low-voltage users. The experimental results show that the proposed non-intrusive arc fault detection and localization method is effective.

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A Novel Method for Detection and Location of Series Arc Fault for Non-Intrusive Load Monitoring

Cited by 5 publications

References 28 publications

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

The Effect of Cable Aging on Surge Arresters Designed by Genetic Algorithm

Non-Intrusive Arc Fault Detection and Localization Method Based on the Mann–Kendall Test and Current Decomposition

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