Novel Approach for Arc Fault Identification With Transient and Steady State Based Time-Frequency Analysis

Zhang, Zhenyuan; Ren, Jie; Tang, Xiaotian; Shi, Jing; Lee, Wei-Jen

doi:10.1109/tia.2022.3170288

Cited by 22 publications

(4 citation statements)

References 35 publications

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“…The remaining SC-CZ and S-CZ cases are equivalent to normal operation and arc faults under resistive load conditions, respectively. The arc extinguishes near the over-zero point of the AC power supply and lingers for a short time at the zero crossing [22] . Therefore, a threshold can be established to differentiate the periodically moving shoulder percentages between the detection currents of SC-CZ and S-CZ.…”

Section: Optimization Control Strategy 41 Detection Algorithm Of Prep...mentioning

confidence: 99%

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Du,

Xu,

Chen

2023

Chin. J. Electr. Eng.

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Outdated testing methods hinder the success rate of carbonized cable preparation in low-voltage arc fault tests, leading to incomplete tests and high failure rates. To address this issue, we finely categorized the preparation results of carbonized cable specimens by analyzing the experimental phenomena during the carbonization process and assessing the impact of high-voltage energization time on the outcomes, presenting a process control strategy aimed at optimizing the preparation results of carbonized cable specimens. This method utilizes three periodic moving algorithms (root-mean-square, average, and shoulder percentage) to classify the cable specimens into four preparation categories: open-circuit carbonization, under-carbonization, short-circuit carbonization, and successful carbonization. The high-voltage energization time during carbonization or secondary carbonization was adjusted to optimize the preparation of the carbonized cables by considering different discrimination outcomes. Finally, the proposed method was tested on a purpose-built carbonized cable experimental platform, which confirmed its effectiveness in differentiating the preparation outcomes of the carbonized cable specimens and improving the success rate of the carbonized cable preparation. The proposed method has significant potential for application in low-voltage arc fault test systems.

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Section: Optimization Control Strategy 41 Detection Algorithm Of Prep...mentioning

confidence: 99%

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Du,

Xu,

Chen

2023

Chin. J. Electr. Eng.

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“…With the increasing number of electrical household equipment, electrical fire has become the number one cause of fire accidents in residential buildings [1]. Great differences exist between low-and high-voltage arcs when it comes to generating medium, arc position, voltage, current, and power.…”

Section: Introductionmentioning

confidence: 99%

Low-Voltage Arc Fault Identification Using a Hybrid Method Based on Improved Salp Swarm Algorithm–Variational Mode Decomposition– Random Forest

Li,

2024

IEEE Access

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The current of the residential series arc fault is affected by the load type, and the fault feature change is not obvious and contains noise. Therefore, the extraction of fault features will affect the arc fault detection results. To solve this problem, an improved salp swarm optimization algorithm combined with variational mode decomposition is proposed to extract the characteristics of current signal, improve the decomposition effect of current signal, and construct a dataset that can fully reflect the characteristics of arc fault. The ReliefF algorithm is designed to combine minimum redundancy maximum relevance to reduce the feature dimension and eliminate redundancy. Finally, the random forest model is used to diagnose the fault, which can quickly detect the fault and does not cause over-fitting. Experiments based on the self-built sample set prove that the arc fault can be quickly detected under different acquisition frequencies and noise environments, and the detection rate is more than 95%.

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“…The rapid increase in load types has led to a decreasing trend in the recognition rate of fault arcs, and some researchers have begun to explore arc detection methods based on artificial intelligence. The Long Short Term Memory Network has shown good arc recognition performance under multiple operating conditions and has been validated on the feature set extracted by the Hilbert transform [10]. Support Vector Machine (SVM) can be used to select the most suitable feature combination as a criterion for fault arcs [11].…”

Section: Introductionmentioning

confidence: 99%

A Low voltage AC fault arc detection method based on multi-feature fusion

Li,

Liu,

Liang

et al. 2024

J. Phys.: Conf. Ser.

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The complex electrical environment makes it difficult for most arc detectors on the market to accurately detect faulty arcs. A universal fault arc detection method based on machine learning (ML) algorithms is proposed in this paper. To address the potential hazards of fault arcs in the power system, firstly, the causes, time-domain, and frequency-domain characteristics of fault arcs were analyzed. Then, an arc experiment platform was built according to national standards, and current data was collected under various load conditions. Finally, the arc detection model was trained based on ML. The main contribution of this study is to explore a feature combination that is not affected by load type interference and has strong generalization ability for identifying fault arcs, clarify the key factors and feature extraction methods for identifying fault arcs, and develop a set of high-precision arc detection methods, providing strong guarantees for the stable operation of the power system.

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Novel Approach for Arc Fault Identification With Transient and Steady State Based Time-Frequency Analysis

Cited by 22 publications

References 35 publications

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Improved Control Strategy for Carbonized Cable Preparation in Low-Voltage Arc Fault Test

Low-Voltage Arc Fault Identification Using a Hybrid Method Based on Improved Salp Swarm Algorithm–Variational Mode Decomposition– Random Forest

A Low voltage AC fault arc detection method based on multi-feature fusion

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