Recognition method of AC series arc fault characteristics under complicated harmonic conditions

Han, Congxin; Wang, Zhiyong; Tang, Aixia; Gao, H.; Guo, Fengyi

doi:10.1109/tim.2021.3051669

Cited by 32 publications

(12 citation statements)

References 18 publications

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“…Based on the calculated results, the maximum voltage sag due to load access is 9V. Using equation (22), we can determine that the set startup threshold U set is 10.8V.…”

Section: ) U Set Settingmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Based on the calculated results, the maximum voltage sag due to load access is 9V. Using equation (22), we can determine that the set startup threshold U set is 10.8V.…”

Section: ) U Set Settingmentioning

confidence: 99%

“…However, it requires synchronously sampling in multiple points, which limits its applicability in low-voltage distribution networks. Artificial intelligence technique also has been introduced to detect arc fault voltage [22]. But it has the same drawback as mentioned in [15][16][17][18].…”

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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show abstract

“…To fulfill the safety requirements, one possibility is to use arc fault detection and protection (AFDP) solutions. To this end, arcs can be detected with specific sensors by applying suitable computer-based algorithms based on pattern recognition techniques, including support vector machine [30], singular value decomposition [31], Fourier transform [32] and neural network approaches [33], [34], among others, to detect and identify arc fault patterns according to their specific characteristics.…”

Section: Parallel Arc Loadmentioning

confidence: 99%

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Riba¹,

Moreno-Eguilaz²,

Ortega³

2022

IEEE Trans. Instrum. Meas.

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Arc faults are serious discharges, damaging insulation systems and triggering electrical fires. This is a transversal topic, affecting from residential to aeronautic applications. Current commercial aircrafts are being progressively equipped with arc fault protections. With the development of more electric aircrafts (MEA), future airliners will require more electrical power to enhance fuel economy, save weight and reduce emissions. The ultimate goal of MEAs is electrical propulsion, where fault management devices will have a leading role, because aircraft safety is of utmost importance. Therefore, current fault management devices must evolve to fulfill the safety requirements of electrical propelled aircrafts. To deal with the increased electrical power generation, the distribution voltage must be raised, thus leading to new electrical fault types, in particular arc tracking and series arcing, which are further promoted by the harsh environments typical of aircraft systems, i.e., low pressure, extreme humidity and a wide range of temperatures. Therefore, the development of specific electrical protections which are able to protect against these fault types is a must. This paper reviews the state-of-theart of electrical protections for aeronautic applications, identifying the current status and progress, their drawbacks and limitations, the future challenges and research needs to fulfill the future requirements of MEAs, with a special emphasis on series arc faults due to arc tracking, because of difficulty in detecting such low-energy faults in the early stage and the importance and harmful effects of tracking activity in cabling insulation systems. This technological and scientific review is based on a deep analysis of research and conference papers, official reports, white papers and international regulations.

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“…Directly build an end-to-end model, which can automatically perform feature extraction and fault classification. Han et al [16] used kernel principal component analysis(KPCA) to separate the noise disturbances in the data. The specific indicators of the 5th and 6th components were used as fault features for SAF detection using SVM.…”

Section: Introductionmentioning

confidence: 99%

“…However, the feature extraction requires a lot of computation and consumes excessive time and memory resources. To advance the characteristics of series arc faults occurring in complex harmonic environments, Han et al [16] utilized kernel principal component analysis, and analyzed the fault signals. The results show that specific indicators in the signal can be used as a basis for SAF detection.…”

Section: Introductionmentioning

confidence: 99%

Three-phase series arc fault detection based on two-dimensional attention PoolFormer

Yu,

Zhang,

Zhao

et al. 2024

Meas. Sci. Technol.

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Series arc faults are a major causation of electrical fires. The complexity of load types in low-voltage distribution systems makes arc faults detection more challenging for three-phase circuits with inverters. To solve this problem, this paper proposes a detection method based on two-dimensional attention PoolFormer. Firstly, a low-voltage three-phase series arc faults data acquisition platform is built to collect the required data. The collected current signals are encoded as pictures through image mapping and projected into a more discriminative space, while increasing the magnitude of the dataset. Subsequently, the two-dimensional attention PoolFormer algorithm model is constructed to fully exploit the feature information between different fault categories. This model has multi-scale parallel convolution to extract features of input samples and perform information fusion. Considering also the ability to seize the location characteristics of fault information well, the two-dimensional attention is designed to be added inside the algorithm, to grasp the precise location information to enhance the performance of the algorithm. Finally, the dataset is fed into the two-dimensional attention PoolFormer model for training and testing. The results show that the accuracy of the method proposed in this paper can achieve 99.36%.

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Recognition method of AC series arc fault characteristics under complicated harmonic conditions

Cited by 32 publications

References 18 publications

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

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

Arc Fault Protections for Aeronautic Applications: A Review Identifying the Effects, Detection Methods, Current Progress, Limitations, Future Challenges, and Research Needs

Three-phase series arc fault detection based on two-dimensional attention PoolFormer

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