Series Arc Fault Detection Research Articles

Series Arc Fault Characteristics and Detection Method of a Photovoltaic System

The DC arc is the main cause of fire in photovoltaic (PV) systems. This is due to the fact that the DC arc has no zero-crossing point and is prone to stable combustion. Failure to detect it in a timely manner can seriously endanger the PV system. This study analyzes the influences of the series arc and the maximum power point tracking (MPPT) algorithm on the PV output characteristics based on the PV equivalent circuit module. The PV voltage and current variation characteristics are obtained when the series arc occurs. The findings indicate that the input voltage of the converter remains unchanged due to the MPPT algorithm before and after the series arc occurs. Furthermore, the PV faulty string output current will drastically decrease when the series arc fault occurs. On this basis, a series arc detection method based on the current change is proposed, suppressing the combustion of the series arc by increasing the target voltage of the MPPT algorithm. The experimental results show that the proposed method can effectively detect and extinguish the series arc in the PV system within 0.6 s. Compared to the other methods, the proposed method can be integrated into the PV system without additional hardware.

Arc_EffNet: A Novel Series Arc Fault Detection Method Based on Lightweight Neural Network

Arc faults can cause a severe electric fire, especially series arc faults. Artificial intelligence (AI)-based arc fault detection methods can have a boasted detection accuracy. However, the complexity and large parameter quantity of the AI-based algorithm will hinder its real-time performance for detecting series arc faults. This paper proposes a lightweight arc fault detection method based on the EffNet module, which can make the algorithm less complex with the same detection accuracy level. An arc fault test platform was constructed to collect arc current data, covering eight types of loads required by the IEC 62606 standard. The raw arc current data are used directly as an input for the proposed algorithm, reducing the module’s complexity. According to features of arc current mainly represented in the time domain, the first and last convolution layers of the EffNet module can be improved. Additionally, the spatially separable convolution is well-tuned and trimmed to achieve a more lightweight and better-performance architecture for arc fault detection called Arc_EffNet. Remarkably, this model achieves an impressive arc detection accuracy of 99.75%. An arc fault detection prototype was built using the Raspberry Pi 4B to evaluate the real-time detecting performance of the proposed method. The experimental results show that the prototype takes a time of about 72 ms to respond to a series arc fault, which can fulfill the requirement of real-time detection for arc faults.

Series Arc Fault Detection Method Based on Signal-Type Enumeration and Zoom Circular Convolution Algorithm

AC series arc fault (SAF) detection under various circuits is always a challenging task because arcing current is affected by different circuit types and fault points. Additionally, normal current is confused as arcing current when non-linear loads are present in the circuit. To cope with these issues, this paper presents a detection method using signal-type enumeration and a zoom circular convolution (CC) (ZCC) algorithm. The generalization ability of the detection method under unknown conditions is improved by means of signal-type enumeration. The impulsive components of current can be divided into stable, periodically impulsive, non-periodically impulsive and hybrid signals by enumeration. Then, given the CC limitations, the ZCC is presented to extract distinguishable features and decrease the computation complexity of CC, and the signal function is partially reconstructed to improve the CC performance. Finally, the presented detection method is analyzed in a laptop and evaluated by TMS320F28335. The online detection results show that the proposed method determined by known single-load circuits has good detection accuracy under unknown conditions and can be achieved in practical application.

Real-Time DC Series Arc Fault Detection Based on Noise Pattern Analysis in Photovoltaic System

This paper presents a method for detecting series arc faults based on noise pattern analysis in photovoltaic systems. The arc detection circuit is required to detect the series arc quickly and not falsely detect the system noise as arc noise. This paper proposes noise pattern analysis to distinguish between system noise and arc noise and prevent false detection. First, periodic feature analysis prevents false detection by periodic noise, such as inverter noise, using the difference in periodic characteristics. Second, zero-range density analysis detects series arc noise using the difference in fluctuation characteristics. An integrated arc fault detection algorithm comprising discrete wavelet transform decomposition, periodic feature analysis, zero-range density, and an iterative loop algorithm is developed. The proposed methods are verified using noise distinction experiments, wherein the injected signal and arc noise are distinguished with high classification precision using the proposed noise pattern analysis method. The developed arc detection algorithm is applied to a real-time series arc detection board based on the TMS320F28335 DSP, and the performance of the series arc detection and false detection prevention is verified using the UL1699B arc detection test circuit and a real PV system.

Series Arc Fault Detection Based on Multimodal Feature Fusion.

In low-voltage distribution systems, the load types are complex, so traditional detection methods cannot effectively identify series arc faults. To address this problem, this paper proposes an arc fault detection method based on multimodal feature fusion. Firstly, the different mode features of the current signal are extracted by mathematical statistics, Fourier transform, wavelet packet transform, and continuous wavelet transform. The different modal features include one-dimensional features, such as time-domain features, frequency-domain features, and wavelet packet energy features, and two-dimensional features of time-spectrum images. Secondly, the extracted features are preprocessed and prioritized for importance based on different machine learning algorithms to improve the feature data quality. The features of higher importance are input into an arc fault detection model. Finally, an arc fault detection model is constructed based on a one-dimensional convolutional network and a deep residual shrinkage network to achieve high accuracy. The proposed detection method has higher detection accuracy and better performance compared with the arc fault detection method based on single-mode features.

Development of an Intelligent Detection Method of DC Series Arc Fault in Photovoltaic System Using Multilayer Perceptron and Bi-Directional Long Short-Term Memory

A DC series arc fault is one of the significant sources of electrical wiring fires in residential buildings. The production of extremely high temperatures may lead to the ignition of nearby combustible materials. The applications of arc fault diagnosis based machine learning are a global interest due to the immense challenge to create an accurate and efficient detection method. In this paper, a detection and classification method using a multilayer perceptron incorporated with Bi-Directional Long shortterm Memory (MLP-BiLSTM) is proposed. In order to achieve this goal, nine series arc fault models are used in conjunction with data from real-world observations for simulation purposes using Power System Computer Aided Design (PSCAD) software. The simulation and experimental results confirm that the accuracy of the proposed detection and classification method reaches 99%, which results in that the methodology is believed to be accurate for DC series arc fault detection and classification in the PV system with relatively high accuracy.

A Transformer Neural Network For AC series arc-fault detection

Detecting series arcing faults in the electrical networks of aircraft can help mitigate dramatic consequences such as fires. Non-Artificial Intelligence algorithms often fail to generalise due to arc fault signals diversity. Most methods in the detection literature use multiple pre-processing (descriptors) associated to a machine learning model (or deep learning). These approaches require to handcraft the descriptors. We propose a deep learning approach without descriptors. We adapted a sequence-based model called a Transformer Neural Network (TNN) model to this time series problem. We repurposed the encoder of the transformer as a sequence-to-sequence model. The model takes as an input a window of electric current, with at least one period of the signals (800 Hz). The output is the label of each point in the input window. This required to propose an original manner of labelling the signals, for which we designed an automated algorithm, increasing the training supervision. Contrary to existing models on aircraft signals, our TNN model has been verified using a public experimental database of electrical-arc signals that simulates aircraft signals (230 V AC at 400−800 Hz, arcs in series with resistive loads). Our model obtained an identification accuracy of 96.3% at a 2% false positive rate. One of the significant performance of our model is that it has the lowest parameter number (2266) that can be found in scientific literature by quite some margin. TNNs are therefore an appropriate candidate for the purpose of arc fault detection, and our labelling method provides a very high temporal resolution of the output.

AC Series Arc Fault Detection Based on RLC Arc Model and Convolutional Neural Network

AC series arc faults in the power system can lead to electrical fires. However, the generalization performance of the determined detection method would be affected under unknown loads, as current features vary with loads. To address this issue, this paper presents a series arc fault detection method based on a high-frequency (HF) RLC arc model and one-dimensional convolutional neural network (1DCNN). By the current transformer used for receiving differential HF features (D-HFCT), current with complex features is firstly simplified and divided into different oscillation-signal types. Since the types of real D-HFCT data are limited, the RLC arc model is used to generate D-HFCT data with various types of oscillation features by adjusting load types, initial phase angles and Bernoulli-sequence frequencies. Then, the simulated data are adopted to train the 1DCNN model. Finally, the trained 1DCNN model can detect series arc faults under different types of real loads. Compared with the 1DCNN method driven by the limited types of real-current data, the presented method shows good generalization ability and achieves 99.33% average detection accuracy under nine types of unknown loads, which benefits from the training of simulated D-HFCT data with abundant HF oscillation features.

Low-voltage series arc fault detection based on ECMC and VB-SCN

The currents of low-voltage series arc faults are non-stationary and nonlinear, so the selection of fault features and the adaptability of the detection algorithm will affect the detection results. In response to these issues, firstly, a feature selection method combining Euclidean distance, classifier criterion, max-relevance min-redundancy and clustering index is proposed, ECMC. This method can filter out weak correlation features and redundant information in the time, frequency and time–frequency domains. The optimal feature data set constructed by ECMC can fully reflect the characteristics of arc faults. Then, a stochastic configuration network (SCN) based on variational Bayesian (VB) optimization, VB-SCN, is proposed. This method uses VB to optimize the output weight and scale function of SCN iteratively. VB-SCN can improve the quality of hidden-layer nodes and the generalization ability of the network while ensuring the adaptive learning of current features, making arc fault detection fast and stable. Finally, the experiments based on the self-built sample set prove that the optimal feature dataset constructed by ECMC has perfect separability. VB-SCN can adaptively realize the accurate and fast detection of arc faults. The detection accuracy is stable between 98.2506% and 100%, which has better robustness and stability.

Series Arc Fault Detection Based on Dual Filtering Feature Selection and Improved Hierarchical Clustering Sensitive Component Selection

Affected by the downstream load of the line, the low-voltage AC series arc fault signal presents nonlinear, non-stationary and random characteristics, which bring great difficulties to the extraction of arc fault features and lead to low arc detection accuracy. To solve this problem, a low-voltage AC series arc fault detection method based on strong discriminative features and sensitive components is proposed. In this work, firstly, the arc fault current signal is collected based on the self-built experimental platform, and the arc fault current components are obtained by the complete ensemble empirical mode decomposition with the adaptive noise (CEEMDAN) method. Secondly, 16 feature indexes based on time window are used to describe the component signals, and an effective feature selection method based on the maximum mutual information coefficient and the significance of feature changes is proposed to realize the mining of highly identifiable features. Then, an improved hierarchical clustering algorithm and a sensitive component selection strategy combining the saliency of feature changes are proposed to eliminate the interference of redundant components. Finally, a strong discriminative feature library of sensitive components of current signal is constructed, and series arc fault detection is realized based on support vector machine. Experimental results show that the proposed method is feasible and effective in arc current feature extraction and fault detection, and provides a reference for low-voltage AC series arc fault detection.

Series Arc Fault Detection Using Regular Signals and Time-Series Reconstruction

This article investigates into signal regularity in ac series arc fault (SAF) detection. The regularity can solve the vital problem that SAF current characteristics change or disappear in unknown multiload circuits. A coupling method is adopted to capture high-frequency differential signals. The coupling signals show that the waveform in single-load circuits closely resembles the waveform in multiload ones. However, the coupling method confuses normal signals with arcing ones in dimmer loads. To address the issue, this article presents a time-series reconstruction method based on spectral features. First, the spectral features are analyzed between fault-like signals and fault signals. According to the spectral features and the desirable margin, the time series is self-adaptively decomposed and reconstructed. Then, the pulse-recognition algorithm is used to extract arcing features of the reconstructed signals. Finally, the detection method determined by single-load circuits is used to identify SAFs in unknown multiload ones. The results show the presented approach has good generalization performance and identification precision under arbitrary circuits.

A Hybrid Approach for Low-Voltage AC Series Arc Fault Detection

In a low-voltage electric distribution network, arc fault presents a high energy density electricity-discharging phenomenon between conductors, which is often caused by aging of electric facilities, loose contacts and terminals, or insulation failure due to internal and external destructions. A large amount of heat may be created during this discharging, which will further cause the risk of fire hazards to mitigate in the residential environment. Currently, many utility grid operators and electricity users are still devoted to seeking effective detection technology for arc fault protection. This paper proposes a hybrid approach that combines discrete wavelet transform (DWT), empirical mode decomposition (EMD), and dynamic time warping (DTW) methods for low-voltage AC series arc fault detection. In DWT, it uses time–frequency domain characteristics of the arc current signal to extract the occurrence of arc fault. In EMD, it decomposes the complex arc fault current signal into a finite intrinsic mode (IMF) signal; then, instantaneous amplitude of IMF signal is obtained by Hilbert–Huang transform (HHT) as a feature for arc fault identification. Firstly, the results of arc fault detections depend on the results from DWT and EMD. When both two methods detect different results, DTW method will be activated, using the similarity measurements between normal and arc fault current waveforms as an assistant measure to determine the occurrence of arc fault. The performance of the proposed approach is tested and validated using various electric appliances, and the results show that the proposed approach can effectively detect low-voltage AC series arc fault.

Arc fault detection on load-side based on sensitive features tracking

The detection of series arc faults using fault current is difficult to overcome the influence of load types, making it difficult to establish a unified fault detection criterion. In contrast, since the arc voltage waveform of fault point is less affected by the load types and is basically a square wave shape, which provide conditions for constructing a unified fault criterion. In terms of the fault information, the fault distortion point of voltage on load-side caused by the arc voltage transition edge provides the position information of the arc voltage transition edge, and its polarity, amplitude and rate of change make it possible to distinguish from the transition edge caused by normal harmonic voltage drop, which provide the theoretical basis for fault detection using the voltage on load-side. Based on the basic analysis of arc voltage waveform features, this paper proposes an arc fault detection method based on load-side voltage sensitive feature tracking for the purpose of identifying the existence of arc voltage transition edges. The method proposed in this paper highlights the transition edge by eliminating the fundamental wave component of the voltage on load-side, the phase areas where the fault distortion points may exist are used as the sensitive area for fault detection, and the identification and tracking of the transition edge is achieved based on the same direction of voltage change, finally, the presence of arc fault voltage is characterized through the polarity, amplitude and rate of transition edge by fusion. The detection method proposed in this paper has a clear physical meaning and has the advantage of being less affected by the load types. Compared with other similar methods, the method proposed in this paper has higher detection sensitivity and stronger ability to distinguish from voltage drop distortion. The experimental results show that the average detection accuracy of the proposed method for faults detection under various loads exceeds 96%, which verifies the effectiveness of the method.

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

Series arc faults cause the majority of household fires involving electrical failures or malfunctions. Low-fault current amplitude is the reason for the difficulties faced in implementing effective arc detection systems. The paper presents a novel arc detection and faulty line identification method. It can be easily used in the low-voltage Alternate Current (AC) household network for arc detection in the Non-Intrusive Load Monitoring (NILM). Unlike existing methods, the proposed approach exploits both current and voltage signal time domain analysis. Experiments have been conducted with up to six devices operating simultaneously in the same circuit with an arc fault generator based on the IEC 62606:2013 standard. Sixteen time-domain features were used to maximize the arc-fault detection accuracy for particular appliances. Performance of the random forest classifier for arc fault detection was evaluated for 28 sets of features with five different sampling rates. For the single period analysis arc, detection accuracy was 98.38%, with F-score of 0.9870, while in terms of the IEC 62606:2013 standard, it was 99.07%, with F-score of 0.9925. Location of a series arc fault (line selection) was realized by identifying devices powered by the faulty line. The line selection was based on the Mean Values of Changes feature vector (MVC50), calculated for absolute values of differences between adjacent current signal periods during the arc fault. The fault location accuracy was 93.20% for all cases and 98.20% for cases where the arc fault affected a single device.

DC Series Arc Fault Detection Method in Photovoltaic System Based on Multiple Frequency Selections for Common-Mode Conductive Voltage

DC series arc faults are one of the main causes of fire hazards in photovoltaic power systems. The common method of the traditional dc series arc fault detection uses wideband current sensors to obtain the arc current signal, extract arc characteristic frequency components, and make intelligent judgments based on numerous samples. This kind of detection method requires high-performance hardware and has a high cost. This article takes the common-mode conductive voltage (CMCV) signal, which is produced by arc faults, as the object and confirms the feature frequency band of the arc CMCV signal based on the spectral analysis. To obtain the feature frequency component and perform frequency reduction at the hardware level, multiple frequency selection and detection circuits were designed, and the feature quantities of the time and frequency domains were extracted from the output of the circuits. Subsequently, a two-dimensional arc fault feature plane was constructed, and an arc/normal similarity criterion and arc fault judgment algorithm were proposed. Finally, this article discusses or experimentally verifies the effectiveness, reliability, and anti-interference ability of the proposed method. The proposed method is low cost, simple, reliable, and effective and provides a novel idea and approach for dc series arc fault detection.

Method of Series Arc Fault Detection Based on Phase Space Reconstruction and Convolutional Neural Network

Series arc fault detection can improve the safety of low-voltage power systems. The existing arc fault detection is mainly based on various indicators of a frequency domain or time-frequency domain transformation for feature extraction, which is difficult to extract comprehensive arc information, resulting in low detection accuracy. This paper presents a method for extracting comprehensive information, combined with a convolutional neural network to detect arc faults. First, the arc fault experimental platform is developed according to the UL1699 standard, and the current signals of various loads under different operating conditions are collected. Then, the current of a single cycle is embedded by coordinate delay, and the distance matrix is calculated by using 50 vectors reconstructed by a single cycle. Finally, a convolutional neural network classification model is designed, which is used to mine the information in the distance matrix to detect series arc faults. The experimental results show that the average accuracy of the method for arc fault identification of various loads is 99.00% and that the sampling frequency is low. It is suitable for lines with different loads and has certain robustness, so this method has the potential to be implemented on hardware.

Smart DC Optimizer for DC Series Arc Fault Detection and Extinguishing

A dc arc faultdetection has been studied to improve the reliability of renewable energies and energy storage systems. In this Letter, the control algorithm of dc optimizer is proposed to detect the dc series arc fault condition between the PV panel and dc optimizer. The operational principle of the detection algorithm considers the characteristics of both the PV panel and arc fault, which can detect and extinguish the dc series arc fault condition. The validity of the proposed algorithm is verified through the simulation and experimental results.

Recursive Least Squares and Adaptive Kalman Filter-Based State and Parameter Estimation for Series Arc Fault Detection on DC Microgrids

In this article, we present a recursive least squares (RLS) and adaptive Kalman filter (AKF)-based state and parameter estimation (SE and PE) for series arc fault (SAF) detection and identification on dc microgrids. It is evident from the state-of-the-art research on dc SAFs that due to the lack of zero crossings and low current of the fault, the detection/identification of a SAF is difficult. Furthermore, due to the unplanned placement of sensors and the effect of SAF’s noise signatures on the adjacent sensors, we present a RLS-based SE for voltages and injection currents. The injection currents and nodal voltages from the states are then used by the AKF for a quick SAF detection, by estimating line admittances on the microgrid. The simulation results, control hardware in loop (CHIL), and experimental results are presented to manifest the SE–PE technique’s potential.

Series Arc Fault Detection Sensor Based on an ABS Rogowski Coil in Medium Voltage

Three types of Rogowski coils were designed and constructed by utilising 3D printing technology: RC1, RC2, and RC3, with the sensors having the same geometrical dimensions but varying number of turns: 20, 50, and 100 respectively. To verify the feasibility and effectiveness of the proposed sensors, a series arc fault generator was set up and high voltage of up to 3-kV was injected. The analysis of the Fast Fourier Transform (FFT) was done by using MATLAB to determine frequency and voltage amplitude. From the analysis, it was determined that the higher the number of turns, the greater the sensitivity. The resonance frequency (MHz) dropped significantly as the number of turns increased. Furthermore, sensor features such as frequency bandwidth and sensitivity are affected by the number of turns, which was thoroughly investigated in this study. The Rogowski coil was created to detect the high frequency component of the series arc fault signal. All the designed sensors reliably detect the series arc fault signal, according to the experimental results.

Series arc fault detection based on continuous wavelet transform and DRSN-CW with limited source data

When a series arc fault occurs in an indoor power distribution system, the temperature of arc combustion can be as high as thousands of degrees, which can lead to an electrical fire. Deep learning has developed rapidly in recent years and is widely used in fault diagnosis. The problem is that the sourced data is challenging to obtain, and few public data sources affect the application of deep learning models in arc fault diagnosis. In order to solve this problem, an arc fault detection method based on continuous wavelet transform and deep residual shrinkage network with the channel-wise threshold (DRSN-CW) is proposed. First, the grayscale images of source data features are obtained by continuous wavelet transform. Then, the feature images are data enhanced to construct the dataset. Finally, the DRSN-CW model is constructed and used to detect arc fault. The results show that the highest accuracy of arc fault detection is 98.92%, and the average accuracy is 97.72%. This method has excellent performance, which provides a new idea for arc fault detection.