Series DC Arc Fault 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

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

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

Photovoltaic DC Series Arc Fault Identification Method Based on Precise Noise Reduction Algorithm

Photovoltaic DC Series Arc Fault Identification Method Based on Precise Noise Reduction Algorithm

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 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.

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

Series Arc Fault Identification in DC Distribution Based on Random Forest Predicted Probability

Series dc arc fault generates arcing noise cross-talk to adjacent electrical loads, making it challenging to identify the arc fault location, which creates a fire hazard that endangers the system’s safety. This study proposes a series arc fault identification method in a dc zonal electrical distribution using Random Forest based local detectors to monitor constant power loads, and output predicted nominal and arc fault probabilities. The predicted probabilities are then sent to a centralized master detector to obtain the final decision. With full communication capability among all local detectors, the master detector makes the final decision using Random Forest algorithms. If there is any disconnection in the communication links, the local detector can operate independently by using the predicted arc fault probability to output the flag signal autonomously, while the master detector continues to operate with other local detectors. The proposed fault identification method is experimentally verified with a zonal electrical distribution testbed that comprises three constant power loads.

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.

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.

Detection and Location of Series DC Arc Fault in Photovoltaic System Based on VMD

A large number of connecting wires and electrical contact points exist in large photovoltaic power generation systems, which can easily cause the occurrence of an arc fault. Failure to detect and isolate faults in time will result in serious fire hazards. In the document, a new approach to fault arc monitoring and positioning built on variational mode decomposition is presented for a variety of series DC arc faults in photovoltaic systems. The time and frequency-domain properties of signals collected under different working conditions are obtained by the use of variational mode decomposition (VMD), and the detection of faults by matching changes in features under the same sub-mode. Finally, a 400KW photovoltaic system model is built by Matlab/Simulink for simulation verification. The outcome indicated that the technique is capable of accurately detecting and locating series DC arc faults.

Dynamic Polynomial Models With ARMA Coefficients Used for Modeling the DC Series Arc Fault in Photovoltaic Systems

Dynamic Polynomial Models With ARMA Coefficients Used for Modeling the DC Series Arc Fault in Photovoltaic Systems

Development of Nottingham Arc Model for DC Series Arc Modeling in Photovoltaic Panels

DC series arc faults are known as a great threat to photovoltaic (PV) systems. Due to component aging and the high level of dc voltage, the occurrence of dc series arc fault is a serious concern in PV generation units. This article deals with driving effective dc series arc models from the original Nottingham arc model using the recorded practical data in a PV system. The original Nottingham arc model has three constant parameters and suffers significant inaccuracy for modeling the series arc faults. To avoid the inaccuracy in the modeling, at first, a Nottingham model is developed that is relevant to voltage and current of the fault with three parameters including one constant model order for exponents of the current and two time-series coefficients. Also, to further improvement in the model's accuracy, a two-fold Nottingham model is proposed that is relevant to voltage and current of the fault with five parameters including two constant model orders for exponents of the current and three time-series coefficients. Finally, a full-time-variant Nottingham model is proposed that mathematically contains three time-series parameters including one time-variant model order of the current exponent. In the full-time-variant Nottingham model, the optimal parameters corresponding to each sliding window of data are utilized. In all the proposed models, the arc coefficients are estimated using a new formulation using the least-squares error technique. Verifying the effectiveness of the proposed models through actual data, the accuracy of the proposed models is compared with the original Nottingham model.

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.

Review of DC Series Arc Fault Testing Methods and Capability Assessment of Test Platforms for More-Electric Aircraft

In the new era of increasingly electric aircraft, the need for reliable and safe electrical systems is more important than ever. In addition, the wide-scale adoption of dc distribution is considered a key enabling technology for more efficient aircraft operation. In this context, arc fault detection devices (AFDDs) have become a topic of interest for the aviation industry with ongoing research to characterize the impact and adequately protect against severe dc series arc faults. Although dc arc faults have been widely investigated for utility applications (such as solar photovoltaic systems), direct adoption of current practices for validating arc detection devices is not straightforward due to the distinct aircraft operating environment. This article provides a first-of-its-kind landscaping exercise of published series arc fault testing based on factors associated with aircraft applications that have the potential to influence the arc characteristics. In addition, an appraisal and associated gap analysis of published arc test platforms is undertaken in order to assess their suitability to support in-depth testing of the impact and mitigation of series arcs within future aircraft dc electrical systems and identify future testing needs, in particular, to better facilitate a comprehensive performance validation of new AFDDs.

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.

Detector of DC Series Arc Fault in a Large Photovoltaic System using Discrete Wavelet Transform

In this paper, the frequency analysis methods can detect the fire accident of the solar arc we propose Discrete Wavelet Transform (DWT). The DWT can express time-axis information even in the frequency domain. In case of DWT algorithm, it was implemented based on Arduino linked with MATLAM. The DC arc accident has been implemented using standard using Rogowski coil. This coil is used to simulate the current sensor for large-capacity PV system application. The constructing and simulated DC series arc generation circuit was verified under actual arc generation conditions. In addition, the performance of the accident detector manufactured was studied The experiment results and its simulation demonstrate, the proposed method is more efficient than others.

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.

Time-Variant Schwarz Based Model for DC Series Arc Fault Modeling in Photovoltaic Systems

DC series arc fault(DCSAF) in photovoltaic systems is a controversial issue owing to the challenges related to its modeling and protection. This article focuses on efficient models based on Schwarz model for DCSAF utilizing actual recorded data. The original Schwarz model has four parameters, containing two coefficients and two model orders, and all four parameters are constant. Despite the original Schwarz model that uses invariant parameters for arc modeling, the proposed models employ a Schwarz model in which the time-variant nature of the arc's parameters is taken into account. The first developed Schwarz model with two constant model orders and two time-variant coefficients is derived in the first part. While the first model provides more capability compared with the original Schwarz modeling, the second developed Schwarz model with full time-variant parameters is also presented in the second part. A novel optimization technique based on Powell's dog leg algorithm is proposed to estimate the arc coefficients in every simulation window. Therefore, the parameters in both modified Schwarz models are time-variant and change every simulation window. As a result, they are classified as a time series, which is examined using the autoregressive moving average process. By actual recorded data, the performance of the proposed models is assessed. Analyses show that the proposed models are more efficient to model DCSAF in comparison with the original Schwarz model.