Series 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

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

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.

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%.

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.

Series arc fault detection based on multi-domain depth feature association

Series arc fault detection based on multi-domain depth feature association

Arc-1DCNN: An enhanced model for series arc fault detection

Series Arc Faults (SAFs) represent a prominent cause of electrical fires in low-voltage distribution systems, often arising from faulty connections or deteriorated insulation. These SAFs occurrences generate high-temperature arcs that endanger electrical system safety. As a result, detecting and accurately identifying SAFs have become crucial concerns. However, the line noise interference and the complexity of the electrical environment make SAFs detection challenging. In this paper, we propose a novel method that combines Wiener filtering with the Arc-1DCNN model to enhance SAFs detection. The method leverages Wiener filtering to enhance current signal, effectively reducing noise interference and providing a more robust dataset for training. To fully exploit the rich high-frequency characteristics of SAFs, Arc-1DCNN incorporates a High-Frequency-Feature-Attention module, enabling the model to capture subtle SAFs anomalies and significantly improving detection accuracy. Experimental validation demonstrates Arc-1DCNN's exceptional performance with 99.94% detection accuracy for SAFs, showcasing its potential for addressing SAFs detection challenges.

Series arc fault detection method based on a residual deformable convolutional network for complex branch circuit

Series arc fault detection method based on a residual deformable convolutional network for complex branch circuit

A Novel Series Arc Fault Detection Method Based on CEEMDAN and IFAW-1DCNN

A Novel Series Arc Fault Detection Method Based on CEEMDAN and IFAW-1DCNN

A series fault arc detection method based on denoising autoencoder and deep residual network

Given the problem that the existing series arc fault identification methods use existing features such as the time-frequency domain of the current signal as the basis for identification, resulting in relatively limited arc detection solutions, and that the methods of directly extracting current signal features using deep learning algorithms have insufficient feature extraction, a new series arc fault detection method based on denoising autoencoder (DAE) and deep residual network (ResNet) is proposed. First, a large number of training samples are obtained through sliding window and data normalization methods, and then high-dimensional abstract feature data are obtained from the fault and normal samples collected in the experiment through denoising autoencoders, converted into grayscale images, and processed in pseudo-color. The single-channel grayscale images are mapped into three-channel color values, and finally, the three-channel values are input into the constructed deep residual network for deep learning training. In the 152 super high-level ResNet, the arc fault recognition rate can reach 99.7%. For loads that have not participated in ResNet network training, the recognition rate can also reach 97.6%.

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location.

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.

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%.

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.

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

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

Research on series arc fault detection method based on the combination of load recognition and MLP-SVM

Research on series arc fault detection method based on the combination of load recognition and MLP-SVM

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.