DC Series Arc Research Articles

Integrated DC arc model and DC arc detection approach based on K‐line diagram and spectrum integral difference

AbstractIn the low voltage direct current (LVDC) systems, the occurrence of DC series arc faults poses a significant threat to the safe operation of the system. This paper develops an accurate arc model for the arc fault simulation. The proposed arc model consists the steady‐state impedance, high frequency characteristics, and dynamic characteristics of the arc. Additionally, this paper proposes an arc detection algorithm combining the K‐line diagram and the spectrum integral difference of the arc current in the LVDC systems. This algorithm can identify four circuit states: normal operation, arc fault, switching action, and load mutation, which addresses the challenge of arc fault detection in complex working conditions. The STM32F407 microcontroller is utilized to design a DC series arc fault detector. Online detection tests demonstrate that the arc faults can be accurately detected and isolated within 37 ms, meeting the requirement of the UL1699B standard, with an accuracy rate of 99.33%. These achievements not only enhance the safety of the LVDC systems but also provide valuable references for the development of arc fault detection technology.

Photovoltaic DC series arc fault detection method based on two-stage feature comprehensive decision

To address the issue of strong randomness and the difficulty in accurately describing fault features of photovoltaic power generation system series arc, a photovoltaic DC series arc fault detection method based on two-stage feature comprehensive decision is proposed. Firstly, to solve the difficulty in selecting fault detection window size due to the non-periodicity and high randomness of DC signals, a signal windowing strategy based on autocorrelation function is proposed. Based on the transient characteristics of arc initiation stage and the steady-state characteristics of arc burning stage, the whole arc stage is divided into transient stage and steady-state stage. Then, in the arc initiation stage, a transient feature description method based on adjacent windows difference (AWD) is designed on the basis of signal windowing, effectively capturing the waveform mutation caused by arc, achieving the fault occurrence window positioning and the effective expression of transient feature. In the arc burning stage, a steady-state feature description method based on energy difference (ED) is designed on the basis of signal windowing and fault occurrence window positioning, effectively capturing the energy difference caused by arc, achieving a significant expression of steady-state feature, and overcoming the misjudgment issues caused by transient feature. Finally, SVMs are used to classify the proposed features, and voting decision is combined to obtain the arc fault detection results. Experimental results show that the proposed method is feasible and effective in the feature extraction and detection of arc fault, providing a valuable approach for photovoltaic DC series arc fault detection.

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

Comparative Analysis of FFT and DWT Techniques Required for UL 1699B-based Photovoltaic DC Series Arc Detection

Comparative Analysis of FFT and DWT Techniques Required for UL 1699B-based Photovoltaic DC Series Arc Detection

Advanced Learning Technique Based on Feature Differences of Moving Intervals for Detecting DC Series Arc Failures

DC microgrids are vital for integrating renewable energy sources into the grid, but they face the threat of DC arc faults, which can lead to malfunctions and fire hazards. Therefore, ensuring the secure and efficient operation of DC systems necessitates a comprehensive understanding of the characteristics of DC arc faults and the implementation of a reliable arc fault detection technique. Existing arc-fault detection methods often rely on time–frequency domain features and machine learning algorithms. In this study, we propose an advanced detection technique that utilizes a novel approach based on feature differences between moving intervals and advanced learning techniques (ALTs). The proposed method employs a unique approach by utilizing a time signal derived from power supply-side signals as a reference input. To operationalize the proposed method, a meticulous feature extraction process is employed on each dataset. Notably, the difference between features within distinct moving intervals is calculated, forming a set of differentials that encapsulate critical information about the evolving arc-fault conditions. These differentials are then channeled as inputs for advanced learning techniques, enhancing the model’s ability to discern intricate patterns indicative of DC arc faults. The results demonstrate the effectiveness and consistency of our approach across various scenarios, validating its potential to improve fault detection in DC 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.

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

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.

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.

Research on DC arc fault detection in PV systems based on adjacent multi-segment spectral similarity and adaptive threshold model

This paper proposes a practical adaptive detection method of series DC arc that could be more adaptable to PV systems’ intricate and complex environment than conventional detecting methods. Firstly, The discharge in a short air gap with unsymmetrical dielectric-covered electrodes will bring additional noise interferences to the current of the circuit where the arc is located and give rise to the alternation of the spectrum of the noise current. By analyzing the current noise spectrum before and after the occurrence of a DC arc fault, it was found that the adjacent multi-segment spectral similarity (AMSSS) characteristic could identify the event of the mark. Secondly, an algorithm architecture using principal component analysis(PCA) of AMSSS and integrated φ-statistic was used to determine an adaptive threshold model. Finally, to validate the fidelity and resilience of the pattern, a photovoltaic plant platform containing 20 PV modules and one three-phase inverter was constructed to acquire relevant data. Then, validation of the adaptive thresholding model was implemented by including several sets of tests generated under different conditions, such as normal, shading, inverter start-up, crosstalk, and arcing conditions. According to the comparative tests, We tentatively conclude that the adaptive model has a relatively strong arc detection capability and high environmental adaptability and could be applied to natural PV systems.

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.

저압직류계통에서 유도성 부하의 차단아크 특성

This paper studies the characteristics of series arcs during the breaking operation of electric contacts of DC switches or socket outlet and plugs on inductive loads. Through experiments of DC series arc on inductive loads, arc extinction distance and arc transient voltage are analyzed and compared with pure resistive loads. This paper presents the arc-safe operation area according to the opening speed of electrode contacts and the time constants of various inductive loads in low-voltage DC(LVDC) systems.

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

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.

저압직류계통에서 용량성 부하의 차단아크 특성

This study investigates the characteristics of series arcs during the breaking operation of DC switch on capacitive loads. Critical capacitance of capacitive loads at which series arc does not occur is analyzed through experiments of DC series arc on capacitive loads. This study presents the estimated critical capacitance of capacitive loads in relation to load power levels and supply voltages in low-voltage dc systems.