Arc Fault Research Articles

Detection performance of arcing fault detectors in the Chinese market according to the Chinese Standard GB/T 31143

There are two sets of standards available in the Chinese market that specify the design requirements and operating tests for arc fault detection devices (GB/T 31143) and arcing fault detectors (GB 14287.4) respectively. While arcing fault detectors (AFDs) are claimed to have passed GB 14287.4 standard tests, it remains unclear whether these AFDs can pass GB/T 31143 standards and effectively detect arc fault signals. This study experiments to explore the operational performance of AFDs in the Chinese market based on GB/T 31143 standards, revealing that not all AFDs can pass this test and provide adequate protection for people’s lives and property. The main reason for the failure is that the arcing characteristics, such as flat shoulders and reduced amplitude, of the signal are easily masked by other branch circuit loads.

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

Under the conditions of a mechanical fault in a motor, mechanical vibration of a specific frequency can be generated. The electrical contact points directly connected to the motor can vibrate at the same frequency. The electrical contact points with poor contact can easily produce a series arc fault under vibration conditions, which affects the reliability of the power supply. In order to detect the series arc fault under different vibration conditions, the arc fault generator is connected between the back end of the frequency converter and the motor. An arc fault experiment under different vibration conditions was carried out and the fault phase current and arc voltage signals were collected. In this paper, the noise-assisted multivariate empirical mode decomposition and the correlation coefficient between each intrinsic mode function are used to select the fault feature signals. Then, the reconstructed signal is input into the series arc fault model combining a multi-scale convolutional neural network and a bidirectional long short-term memory network for training. The research results show that the series arc fault detection method proposed in this paper can effectively detect the series arc fault and can preliminarily identify the type of motor fault causing the mechanical vibration of the motor; the model has good noise immunity and generalization.

DC Series Arc Fault Diagnosis Scheme Based on Hybrid Time and Frequency Features Using Artificial Learning Models

DC series arc faults pose a significant threat to the reliability of DC systems, particularly in DC generation units where aging components and high voltage levels contribute to their occurrence. Recognizing the severity of this issue, this study aimed to enhance DC arc fault detection by proposing an advanced recognition procedure. The methodology involves a sophisticated combination of current filtering using the Three-Sigma Rule in the time domain and the removal of switching noise in the frequency domain. To further enhance the diagnostic capabilities, the proposed method utilizes time and frequency signals generated from power supply-side signals as a reference input. The time–frequency features extracted from the filtered signals are then combined with artificial learning models. This fusion of advanced signal processing and machine learning techniques aims to capitalize on the strengths of both domains, providing a more comprehensive and effective means of detecting arc faults. The results of this detection process validate the effectiveness and consistency of the proposed DC arc failure identification schematic. This research contributes to the advancement of fault detection methodologies in DC systems, particularly by addressing the challenges associated with distinguishing arc-related distortions, ultimately enhancing the safety and dependability of DC electrical systems.

A DC Arc Fault Sensor With Leftover Gated Recurrent Neural Network in Consumer Electronics

A DC Arc Fault Sensor With Leftover Gated Recurrent Neural Network in Consumer Electronics

Experimental Research on Heat Transfer and Ignition Characteristics of Series Fault Arc Combustion in 220VAC Power System

ABSTRACT This paper studied the heat transfer and ignition characteristics of series fault arcs combustion in the 220VAC power system. Series fault arcs with seven currents (4-30A) were generated to investigate the outward heat transfer characteristics of arc combustion. The distance between the fault arc and the combustibles was changed in experiments to study ignition characteristics. Electrode temperature and arc near-domain temperature, ignition time, and temp-erature of the combustible surface were measured. The results show that the ambient air temperature rise (T-T 0) is inversely proportional to the radial distance from the fault arc (r), and there is a linear relationship between the temperature (T-T 0) and the current divided by the radial distance from the fault arc (I/r). As for the temperature of the electrode, the larger current causes a higher electrode temperature. A semi-infinite solid ignition model is used to deal with the ignition process of the combustibles. Experimental results show that there is a good linear relationship between the current (I 2) and the ignition time ( t ig − 1 / 2 ). This study quantitatively evaluates the hazards of arc combustion and is of great significance for explaining the ignition mechanism of an arc-type electrical fire.

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

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%.

Alternative methods for internal arc tests on 12 kV and 24 kV metal-enclosed switchgears with compact RMU

Arcing due to an internal arc fault test is carrying out in order to demonstrate the capability of the metal-enclosed switchgear to withstand to stresses generate by electric arc. Firstly, it is aimed the limitation of the destructive effects of the enclosures and protection of the persons near the metal-enclosed switchgear. The behaviour of the metal-enclosed switchgear during the test is pointed out by video recordings, which lead to the evaluation of the test result together with the observations stated after test on metal-enclosed switchgear and indicators also. The paper presents two alternative methods for verification at electric arc action of metal-enclosed switchgears according to international standards.

High-impedance arc fault modeling for distribution networks based on dynamic geometry dimension

Accurate modeling of high-impedance arc fault (HIAF) is of great significance for studying the characteristics of arc fault and suppressing its harm. In traditional arc models, the arc column is usually considered a cylindrical channel with constant length and diameter. However, the arc-burning process is susceptible to the environment. The changes in arc length and diameter present complex characteristics. Therefore, this study proposes a new HIAF model for distribution networks based on the arc's dynamic geometry dimension (DGD). First, the arc length, diameter, and field strength expressions are improved based on the classical cybernetic model. Next, an automatic parameter optimization method for the DGD model is proposed, and then this model is compared with existing advanced models. After that, the effects of the variable parameters of this model on arc characteristics are analyzed. Finally, the practical application effect of this model is tested. The experimental results show that the DGD model can accurately describe the dynamic arc development process, approximate the given arc waveform closely, and generate high-quality samples, which has certain advantages.

Low-voltage AC series arc fault detection based on Fisher-mutual information feature selection

The detection of multi-feature fusion is a crucial approach to address the issue of series arc fault detection. Effective feature selection plays a vital role in enhancing the accuracy of the classifier and reducing system complexity. In this study, a feature selection algorithm based on Fisher-mutual information is proposed to tackle the problem of feature selection in multi-feature fusion detection. This algorithm utilizes the characteristics of arc fault voltage source to construct a feature pool. The Fisher-score algorithm and mutual information algorithm are employed to construct an optimal feature subset. The feature subset undergoes rough selection by retaining key features of the classifier and fine selection by eliminating redundant features. Experimental results and comparisons with related methods demonstrate that the proposed feature selection method significantly enhances the classifier's recognition accuracy, reduces classification and recognition time, diminishes the feature dimension, and outperforms other existing methods.

A Simplified Current Feature Extraction and Deployment Method for DC Series Arc Fault Detection

DC series arc fault (DSAF) is one of the main causes of low-voltage DC (LVDC) distribution system fire accident. Traditional methods use time-frequency domain features to make judgments directly or use machine learning to further classify them. In this paper, a method is proposed to simplify the feature extraction process while ensuring accuracy. Firstly, a fully automated process is used to establish DC arc datasets. A composite bandpass filter (CBF) is designed to extract the typical frequency segment of DC arc. Besides, an arc detection neural network (ArcNN) based on temporal convolution network (TCN) is proposed to extract current waveform features. Principal component analysis (PCA) is used to process these features to reduce correlation. Finally, a single hidden layer neural network (SHLNN) is used as classifier. The database is collected from different scenarios and working conditions. By measuring the DC raw current, the arc fault detector (AFD) can achieve a test set accuracy of 99.88% at a sampling rate of 250 kHz. The model is also deployed on Jetson Nano with an average real-time detection of 0.15 s/sample.

Diagnosis of DC series arc fault based on multiple wavelet transform and optimal fractional wavelet energy entropy.

In the DC distribution system, the propagation of arc noise can interfere with normal lines, and accurate and timely diagnosis of the location of series arc fault (SAF) is a challenging problem. In this article, a SAF diagnosis method is proposed from a system perspective, which can accurately identify the fault line. First, multiple wavelet transform is used to decompose the currents of different lines, and the fractional wavelet energy entropy is extracted to construct the feature vector. Then, random forest is employed to analyze the importance of features and to select the optimal features. Finally, a kernel extreme learning machine can fuse the features and output the diagnosis results. The offline experimental results indicate that the proposed method has a diagnosis accuracy of 99.82%, which is higher than those of nine comparison methods, and the effectiveness and advancement of the proposed method are verified. The online experimental results show that the proposed method can diagnose SAF within 110ms, and the diagnosis speed is able to satisfy the requirements of UL1699B. Moreover, under transient conditions, the proposed method can effectively avoid false alarms and maintain stability.

A series arc fault diagnosis method based on random forest model

A series arc fault diagnosis method based on random forest model

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

Series Arc Fault Identification Method Based on Lightweight Convolutional Neural Network

A series arc fault diagnosis method based on random forest model

A series arc fault diagnosis method based on random forest model

A Novel Mathematical–Physical Arc Model and its Application to the Simulation of High-Impedance Arc Faults in Distribution Networks

A Novel Mathematical–Physical Arc Model and its Application to the Simulation of High-Impedance Arc Faults in Distribution Networks

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

Explainability Approach-Based Series Arc Fault Detection Method for Photovoltaic Systems

A Probabilistic Approach to Series Arc Fault Detection and Identification in DC Microgrids

A Probabilistic Approach to Series Arc Fault Detection and Identification in DC Microgrids

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

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

Arc Fault Diagnosis Method for Brush Slip Ring System of Doubly-Fed Induction Generator Based on Image Recognition

Arc Fault Diagnosis Method for Brush Slip Ring System of Doubly-Fed Induction Generator Based on Image Recognition

DC Series Arc Fault Detection and Self- Extinguishing Method With Power Stage Design of Module Level Power Electronics

DC Series Arc Fault Detection and Self- Extinguishing Method With Power Stage Design of Module Level Power Electronics