Arc Fault Detection Research Articles

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

DC Series Arc Fault Detection Method With Resonant Filter Design for PV Systems

Characteristics and fire-inducing risk analyses of arc faults in low-voltage electrical systems

Arc fault is a prevalent phenomenon in low-voltage residential power systems and the main cause of electrical fires. This paper explores the fire risks associated with arc faults and employs standards from arc fault detection devices (AFDD) to create an arc fault experimental system complemented by an integrated multi-sensor system for ignition related data collection. The objective is to synthetically examine the dynamic characteristics and ignition mechanisms of arc faults. This paper investigates the generation mechanisms, waveforms, and energy characteristics of arc faults, including waveform distinctions across different load types and patterns of energy variation during arcing. Data from the multi-sensor system become the cornerstone of the correlation analysis between the current, energy, and ignition phenomena, facilitating the construction of an ignition probability model based on maximum likelihood estimation. The reliability of the model is verified across various datasets with a 95 % confidence interval, offering a quantitative fire-inducing risk assessment of arc faults. Furthermore, the paper assesses the AFDD standards’ break time limits and evaluates the performance of seven AFDD prototypes based on the proposed model, demonstrating the effectiveness of this technology in mitigating the risks of fires induced by arc faults. This study systematically analyzes the behaviors and ignition mechanisms of arc faults, thereby providing scientific evidence and data that support the enhancement of arc fault detection technologies and the development of fire prevention strategies.

Simulation Environment for the Testing of Electrical Arc Fault Detection Algorithms

Simulation Environment for the Testing of Electrical Arc Fault Detection Algorithms

Research on Low-Voltage Arc Fault Based on CNN–Transformer Parallel Neural Network with Threshold-Moving Optimization

Low-voltage arc fault detection can effectively prevent fires, electric shocks, and other accidents, reducing potential risks to human life and property. The research on arc fault circuit interrupters (AFCIs) is of great significance for both safety in production scenarios and daily living disaster prevention. Considering the diverse characteristics of loads between the normal operational state and the arc fault condition, a parallel neural network structure is proposed for arc fault recognition, which is based on a convolutional neural network (CNN) and a Transformer. The network uses convolutional layers and Transformer encoders to process the low-frequency current and high-frequency components, respectively. Then, it uses Softmax classification to perform supervised learning on the concatenated features. The method combines the advantages of both networks and effectively reduces the required depth and computational complexity. The experimental results show that the accuracy of this method can reach 99.74%, and with the threshold-moving method, the erroneous judgment rate can be lower. These results indicate that the parallel neural network can definitely detect arc faults and also improve recognition efficiency due to its lean structure.

AC Series Arc Fault Detection for Wind Power Systems Based on Phase Lock Loop With Time and Frequency Domain Analyses

AC Series Arc Fault Detection for Wind Power Systems Based on Phase Lock Loop With Time and Frequency Domain Analyses

Wavelet-based arcing signal source localization algorithm using a compact multi-square microstrip antenna

Ensuring power system safety involves effective arc fault detection and localization. Existing devices struggle to differentiate between normal and abnormal conditions, especially in confined spaces, posing precision challenges. Strategically placing antennas around the arc helps detect electromagnetic radiation, even in limited areas, enabling valuable data collection for real-time monitoring. To address these challenges, this paper proposes integrating experimental work using a compact multi-square microstrip antenna and signal processing techniques. The study compares the effectiveness of two signal processing approaches: Discrete Wavelet Transform (DWT), and Continuous Wavelet Transform (CWT). These techniques separate genuine arc signals from background noise by identifying unique characteristics and isolating the dominant frequency. The Time of Arrival (ToA) is measured and used in Least Square and Gauss-Jordan Elimination methods to calculate the arc source location. The outcomes illustrate the precision of the proposed model in detecting and pinpointing arc source signals, with error margins ranging from 0.0615 to 0.0713 m for the CWT technique, 0.0688 to 0.0789 m for the DWT technique, and 0.0799 to 0.0844 m from the actual signal captured. These results hold promise for enhancing the integration of experimental approaches in assessing arcing conditions, thereby addressing challenges in insulation systems.

Series DC Arc Fault Detection in DC Microgrids Based on Distributed Unknown Input Observers

Series DC Arc Fault Detection in DC Microgrids Based on Distributed Unknown Input Observers

Early detection of arc faults in DC microgrids using wavelet-based feature extraction and deep learning

Early detection of arc faults in DC microgrids using wavelet-based feature extraction and deep learning

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

Abstract 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.

Lightweight Low-Voltage AC Arc-Fault Detection Method Based on the Interpretability Method

Electrical fires are frequently caused by low-voltage AC series arc faults, which can result in significant injuries and property damage. The installation of arc-fault detection devices is mandated or recommended in many regions and countries across the world, yet the current devices’ detection accuracy is insufficient to completely eliminate the risk posed by arc faults. The method based on artificial intelligence is a solution with high detection accuracy, but the AI model is a ‘black box’. When a misjudgment occurs, the cause of the model error cannot be found fundamentally, and the modification and light weight of the model also presents significant difficulties when using the approach. Given the aforementioned issues, this research proposes a novel lightweight low-voltage AC arc-fault detection method based on the explainability approach. By applying the attention mechanism approach and performing a visual analysis, the contribution of arc features to model detection is determined. Model input data optimization and model structure simplification are achieved at the same time as increased model detection accuracy. Ultimately, an experimental prototype for arc-fault detection is designed and validated. Test results demonstrate the effectiveness of the method by demonstrating that the lightweight model maintains 99.69% detection accuracy, even after optimizing the input data by 80% and reducing the model parameters by 51.52%.

AI-enhanced time–frequency domain reflectometry for robust series arc fault detection in DC grids

The generation of series arc fault (SAF) within DC grids poses a significant hazard, potentially leading to electrical fires. However, developing a diagnostic method that is robust to the external environment and noise remains challenging. Conventional reflectometry methods are unable to discern minute impedance fluctuations at cable joint, as they analyze reflected signals generated at points of impedance mismatch to locate faults.This paper presents a new version of reflectometry, which merges artificial intelligence (AI) with the time–frequency domain reflectometry (TFDR) technique. In this study, we designed a structure that combines denoising autoencoders (DAE) with autoencoders (AE) to secure robust SAF detection performance against noise such as ambient environmental changes and weather fluctuations. Furthermore, the time-series generative adversarial network (TimeGAN) was utilized to generate synthetic data. Three experiments were conducted to verify the performance of the proposed method. (1) According to the UL1699B safety standard, an experiment was conducted under laboratory conditions to check whether a SAF could be detected within a maximum of 2.5s immediately after a SAF occurred. (2) To simulate external environmental conditions, a system connected to an inverter and a photovoltaic (PV) simulator was utilized to emulate the occurrence of SAFs. The manipulation of the PV simulator allowed for the simulation of solar irradiance changes due to weather variations and power fluctuations occurring immediately after the system’s power was turned on. This created an experimental setup that closely resembled actual PV systems, where the performance of the proposed algorithm was validated. (3) Finally, a 1kW capacity PV system was constructed, and SAF was simulated within it. The system was tested using four SAF detection methods, and the experimental results were compared and validated.

DC Series Arc Fault Detector With Differential DWT and Variable Threshold Method for Photovoltaic Systems

DC Series Arc Fault Detector With Differential DWT and Variable Threshold Method for Photovoltaic Systems

A Study on the Operation Characteristics of Arc Fault Detection Device under Arc Generation

According to the National Fire Agency's statistics in South Korea, the number of fires caused by electrical factors is increasing every year. Residual current devices and molded case circuit breakers are used to prevent electrical fires, but it is difficult to prevent electrical fires caused by arc generation. To prevent electrical fires caused by arc generation, studies on arc fault detection devices (AFDDs) and product development for commercial purposes are being conducted. However, the performance of AFDDs under real-life conditions has not been sufficiently verified. In this study, we conducted experiments on three types of AFDDs to verify their performance when a series arc is generated. Moreover, we performed arc waveform measurement and analysis. For the series arc test, we used a series arc generator based on the KS C IEC 62606 standard. We conducted arc-breaking experiments of the AFDDs with resistive and inductive loads and performed waveform measurement and analysis of the arc voltage and current.

Series alternating current arc fault detection method based on relative position matrix and deep convolutional neural network

The detection of series arc faults is crucial due to the potential safety hazards caused by cable aging and breakage. This paper proposes an arc fault detection framework that utilizes the relative position matrix and deep convolutional network to accurately detect alternating current series arc faults. In this framework, the time series data is first converted into a relative position matrix and then visualized as a two-dimension image. This enables the characterization of singular values and flat shoulders in the arc current, making it easier to extract significant features for the models. Next, a Residual Network with hybrid attention mechanisms model is constructed and the images are fed into the model to determine whether an arc fault has occurred in the current signal. Finally, the data is collected using the experimental platform, and the relative position matrix is constructed with different dimensionality reduction factor. The appropriate dimensionality reduction factor is selected through experimental comparisons. In addition, the effectiveness of the proposed method is verified by measured data, and the proposed model is compared with four other commonly used network models, which demonstrates the superiority of the proposed model in terms of detection performance. The proposed method achieves a detection accuracy of 98.74%.

Voltage‐based fault arc detection based on PCA‐RF

SummaryThe arc fault characteristics of certain loads lack significance, making it difficult to efficiently detect the line current characteristics. This research presents a novel approach for detecting arc faults using a combination of principalc analysis (PCA) and Random Forest (RF) based on voltage measurements. The time‐domain eigenvalues of the load terminal voltages of single and mixed loads are initially extracted during both arc fault and normal operation. Principal component analysis is then conducted on a subset of these eigenvalues. The skewness and magnitude features of the resulting principal components and load terminal voltages are utilized as inputs for the Random Forest algorithm. After training the model, classification results are obtained. Ultimately, it is contrasted with techniques such as rime optimization algorithm‐multilayer perceptron (RIME‐MLP), convolutional neural network‐gated recurrent unit‐SE attention (CNN‐GRU‐SE), and Kepler optimization algorithm‐support vector machine (KOA‐SVM). The results demonstrated that the approach exhibits superior accuracy and a reduced false alarm rate.

Series‐arc‐fault diagnosis using feature fusion‐based deep learning model

AbstractThis paper describes the detection of series arc faults, which constitute the major cause of electrical fires, in a power distribution system. Because the characteristics of series arc faults change considerably depending on the load type, their accurate detection and analysis are difficult. We propose a series‐arc‐fault detector that uses a transfer learning (TL)‐based feature fusion model. The model is trained stagewise for various features in the time and frequency domains using a one‐dimensional convolutional neural network combined with a long short‐term memory model that uses an attention mechanism to accurately detect arc‐fault features. To enhance the reliability of the proposed model, we implement an arc‐fault generator compliant with the UL1699 standard and acquire high‐quality data that suitably reflect the real environment. Experimental results show that the proposed model achieves an accuracy of 99.99% in classifying series arc faults for five different loads. Hence, a performance improvement of approximately 1.7% in classification accuracy is reached compared with a feature fusion model that does not incorporate TL‐based model transfer and the attention mechanism.

Integration of wavelet packet transform, residual convolution neural network, and support vector machine for series arc fault detection

Series arc fault (SAF) poses a great challenge to the safe and stable operation of civil low-voltage distribution systems. For the accurate and rapid detection of SAF, this article proposes an SAF detection method using wavelet packet transform (WPT), residual convolution neural network (RCNN), and support vector machine (SVM). First, the raw current signal is decomposed into four wavelet components based on WPT. Then, the 1-D wavelet components are converted into 2-D matrices. Afterward, the matrices are input into RCNN through different channels. Finally, the detection results can be yielded by SVM. The effectiveness of the proposed method is verified based on offline experiments. The average detection accuracy of the proposed method is 99.72%, which is higher than that of the eight comparison methods. Moreover, the results of online experiments indicate that the detection time of the proposed method is less than 100 ms and can satisfy the requirement of standard 1699.

Photovoltaic DC arc fault detection method based on deep residual shrinkage network

Photovoltaic DC arc fault detection method based on deep residual shrinkage network

Progress of Photovoltaic DC Fault Arc Detection Based on VOSviewer Bibliometric Analysis

This paper presents a review of research progress on photovoltaic direct current arc detection based on VOSviewer bibliometric analysis. This study begins by introducing the basic concept and hazards of photovoltaic DC arcing faults, followed by a summary of commonly used arc detection techniques. Utilizing VOSviewer, the relevant literature is subjected to clustering and visualization analysis, offering insights into research hotspots, trends, and interconnections among different fields. Based on the bibliometric analysis method of VOSviewer software, this paper analyzes the articles published in the last 10 years (2014–2023) on photovoltaic DC fault diagnosis. We analyzed the specific characteristics of 2195 articles on arc failures, including year of publication, author, institution, country, references, and keywords. This study reveals the development trend, global cooperation model, basic knowledge, research hotspots, and emerging frontier of PV DC arc. Future research directions and development trends for photovoltaic DC arc detection are proposed which provides valuable references for further studies and applications in this domain. This comprehensive analysis indicates that photovoltaic DC arc detection technology is expected to find broader applications and greater promotion in the future.

Series AC Arc Fault Detection Method Based on L2/L1 Norm and Classification Algorithm

Series AC Arc Fault Detection Method Based on <i>L2/L1</i> Norm and Classification Algorithm