Arc Fault Research Articles

Enhancing Fault Location Accuracy in Transmission Lines Using Transient Frequency Spectrum Analysis: An Investigation into Key Factors and Improvement Strategies

Fault location estimation in transmission lines is critical for power system reliability. Various methods have been developed for this purpose, among which transient frequency spectrum analysis (TFSA) stands out as a recent method based on travelling wave (TW) theory. TFSA determines the fault location by analyzing the frequency spectrum of transient currents and/or voltages at the instant of the fault, offering advantages such as independence from fault impedance and the ability to locate faults with one-side measurements. Despite its success in fault location, TFSA has several considerations that warrant detailed investigation. This study explores the effects of source inductance, series compensation, fault arc, and current transformer (CT) characteristics on transient frequencies. Additionally, the impact of noise on TFSA results is examined. The new proposed source inductance compensation method can reduce the error of 6.55% to 0.88%, where the same error can be reduced to 3.45% with the compensation method given in previous study. Strategies to enhance accuracy are discussed and compared to previous studies, including a proposed detection approach providing appropriate data size and precise wave propagation speed calculations. These findings contribute to a deeper understanding of TFSA’s limitations and inform practical improvements for fault location accuracy in power transmission systems.

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.

Short‐circuit current of a hydropower plant with consideration of constant switching and fault arc voltages

AbstractThe correct generator circuit breaker (GCB) dimensioning is essential for the safe and reliable operation of a power plant or generation system. The dimensioning is usually based on standardized calculation methods according to standards (IEC standard 60909‐0, IEC/IEEE standard 62271‐37‐013, IEEE Std C37), often supplemented by selected transient calculations. A non‐systematic approach can often be observed here, which does not adequately take into account significant influencing variables or operating states of the generator. This article therefore systematically examines various parameters that influence the short‐circuit current components of the generator and are relevant for the dimensioning of the generator circuit‐breaker: short‐circuit angle, operating point, impedance ratios, phase clearing, switching arc, and fault arc. The results of the current parameters most relevant to the dimensioning of the GCB were then compared for different calculation methods. Special attention was paid to the effect of the switching and fault arc, which were modelled as a constant arc voltage, and its effect on the short‐circuit currents is systematically recorded. This work aims to summarize all relevant variables that influence the generator short‐circuit current and are relevant for the dimensioning of the GCB and to present the different results based on a short‐circuit calculation according to the standard and transient calculation to create a basis for a proper dimensioning of the generator circuit breaker.

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.

An enhanced algorithm for detection of HIAF in active distribution networks and real-time analysis

Identifying a downed conductor in distribution is challenging due to the insignificant variations in the current signal. Most of the distribution system faults are identified using overcurrent and earth fault protection. Fault path resistance decides the current magnitude while high resistance path may create timely issues in identifying a fault. Downed conductor and high resistance faults create arc in between the ground and conductor. Due to this the fault at the location is highly nonlinear with reduced magnitude. These faults are known as high impedance arcing fault (HIAF). Such faults can be identified effectively by conducting harmonic analysis of the signals. But in a modern distribution system apart from other renewable sources, the integration of electric vehicle (EV) may create HIAF detection challenges due to the inclusion of inverters and switching harmonics associated with EV. Considering the available advanced architecture of modern microgrid system, an effective solution is proposed in this work to securely detect HIAFs. The method is robust and highly reliable for HIAFs. The system stability and continuity can be unaffected with the help of the proposed method in case of various non-fault or transient/switching conditions. Comparison with other profound techniques is done to show the superiority of the proposed method.

Arc Ignition Methods and Combustion Characteristics of Small-Current Arc Faults in High-Voltage Cables

High-voltage cables will continue to operate for a period of time in the event of a small current arc fault, which poses a risk of fire. Two simulated ignition methods, moving electrode and melting fuses, are proposed to analyze the ignition characteristics of low-current arcs. The ignition test was carried out, and the combustion effect was compared. The results indicate that the moving electrode ignition method can achieve long-distance arc ignition test when the current is small and is suitable for simulating the arc ignition situation of cable outer protective layer damage. By controlling the movement speed, it can be ensured that the arc will not be interrupted during the electrode movement process. However, the arc is difficult to sustain using the fuse melting method when the current is small and the distance is long. The fuse melting method is suitable for simulating insulation breakdown situations. The results show that the critical arc duration for cable ignition under five different current conditions of 2–10 A is 28 s, 21 s, 14 s, 9 s, and 4 s, respectively. The maximum height of the cable flame under 2–10 A arc current is 9–52 cm and 16–63 cm, respectively, when the arc duration is 50 s and 100 s. The self-ignition time of the cable after the arc extinguishing is 8–95 s and 14–261 s, respectively. The maximum temperature of the cable flame is positively correlated with arc current, and the maximum flame temperature of the cable under 2–10 A arc current is 540–980 °C. Based on the actual current monitoring data in cable tunnels, the research results can provide reference for the risk assessment and protection of cable tunnel fires.

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.

Extinguishing High-Energy Fault Arc in Transformer via Current Transfer With a Triggered SF6 Gap in Sub-Millisecond

Extinguishing High-Energy Fault Arc in Transformer via Current Transfer With a Triggered SF₆ Gap in Sub-Millisecond

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground fault

Testing the operation of protection devices against parallel arc breakdown and spark gaps in the event of a ground faultTHE PURPOSE. Develop a laboratory rig for testing parallel arc fault and spark gap protection devices for tripping in the event of a ground fault. Conduct laboratory rig tests with subsequent development of requirements for the use of parallel arc fault and spark gap protection devices for tripping in the event of a ground fault.METHODS. Mathematical and statistical methods of analysis were used to achieve the stated objective. Laboratory tests of parallel arc fault and spark gap protection devices for different network parameters for correct tripping in the event of a ground fault.RESULTS. Laboratory tests of parallel arc fault and spark gap protection devices for correct tripping in the event of a ground fault were conducted with different network parameters. The oscilloscope recorded the shape of the voltage and current curve in the event of a ground fault and the tripping time of the devices. The tested devices demonstrated the ability to recognize and disconnect a ground fault. The tripping time of the devices varies, however, does not exceed the established requirements.CONCLUSION. A laboratory stand has been developed for testing parallel arc breakdown and spark gap protection devices for operation during a ground fault. This stand makes it possible to test devices for adequate operation during a ground fault with subsequent certification. The tests conducted demonstrate the need to develop mandatory requirements for the device to standardize their operating algorithm. Therefore, the use of this protection device can contribute to safer operation of low-voltage electrical networks by preventing emergency situations and reducing the risk of fires. This is an important technical aspect for improving the reliability and safety of power supply.

Intelligent Identification Method of Low Voltage AC Series Arc Fault Based on Using Residual Model and Rime Optimization Algorithm

Intelligent Identification Method of Low Voltage AC Series Arc Fault Based on Using Residual Model and Rime Optimization Algorithm

Failure mechanism and thermal runaway behavior of lithium-ion battery induced by arc faults

As the widespread of lithium-ion battery systems such as electric vehicles and energy storage systems, the number of safety incidents due to electrical faults are increasing. Many accident reports have demonstrated that arc faults have become one of the main triggers of LIB system accidents, however, the related studies are inadequate. In this study, an arc imitation system is employed to investigate the influence of different arc energies on battery safety valve, as well as the electrochemical characteristics of faulty batteries. The results show that the minimum arc power to breach the safety valve ranges from 110 to 441 W. The maximum temperature rise rate on the battery surface can exceed 15 °C/s with arc power of around 1000 W. Further, the testing of in-situ and ex-situ indicate the faulty batteries undergo degradation and failure due to that moisture in the air enters the battery interior, resulting in increased internal resistance, loss of active materials and cyclable lithium. Finally, the faulty battery has no valve opening during thermal runaway, and the ignition time is four hundred seconds earlier than that of the normal battery, indicating more severe fire dangers. The results are valuable for safety design of battery systems in relation to arc faults, as well as the characteristic for fault detection and early warning.

Series arc‐fault diagnosis using convolutional neural network via generalized S‐transform and power spectral density

AbstractIt is difficult to identify an arc fault accurately when the loads on the user side are more complicated, which hinders the development of low‐voltage monitoring and pre‐warning inspection. This study acquired a series of arc‐fault signals according to IEC 62606. The main time‐frequency features were strengthened with high efficiency by applying the generalized S‐transform to them with a bi‐Gaussian window. Further, the power spectrum density determination allowed for the detection of imperceptible high‐frequency harmonic energy reflections, thus increasing the rate of arc‐fault diagnosis and making it suitable for arc‐fault monitoring of non‐linear loads. The final samples were trained and classified using a 2D convolutional neural network and the overall accuracy of identification was observed to be 98.13%, which involved various domestic loads, thus providing a reference for follow‐up arc‐fault monitoring and inspection research.

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

Pyrolysis and temperature characteristics of the gas produced by power-frequency arc in transformer oil

Oil-immersed transformers are widely used in power systems. When an arc fault occurs inside a transformer, the surrounding oil is rapidly pyrolyzed. These high-temperature, flammable gases are compressed into a high-pressure bubble, which is likely to cause tank deformations, explosions, and fires. Currently, related simulations on tank internal pressure and transformer fires are based on a constant gas composition, a constant gas production rate of 0.1 L/kJ, and a constant bubble temperature of 2000 K. This paper deals with a case of a power-frequency arc (5 kA, 30 mm, 80 ms) in transformer oil. The time-varying characteristics of pyrolyzed gas production, composition, and temperature are experimentally investigated. The percentage of hydrogen changes considerably, decreasing from 85.2 % to 70 %. The overall gas production rate increases slowly from 0.0574 L/kJ in the first 2.5 ms to 0.0982 L/kJ in 80 ms. The local temperature inside the main bubble (30 mm from the arc) decreases gradually from 2200 K to 1400 K, while the average bubble temperature decreases from 2550 K to 550 K. This case and these results provide an experimental basis for the parameter selections in the simulations about transformer internal pressure and fire progression.

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.

Experimental and Modeling Study of Arc Fault Induced Thermal Runaway in Prismatic Lithium-Ion Batteries

With the widespread application of electrochemical energy storage technology, the safety issues of lithium-ion batteries have garnered significant attention. The issue of arc faults resulting from electrical failures is especially critical, as it can lead to catastrophic battery disasters. Therefore, this paper first established an arc testing platform and conducted experiments on top cover and body of prismatic lithium-ion batteries to analyze the thermoelectric characteristics between arc and battery. Under experimental conditions of 300 V and 15 A, it was found that arcs can induce thermal runaway in batteries. Subsequently, based on the experimental conditions, a mathematical model was established to induce thermal runaway in batteries through an equivalent method of arc heat source. By comparing the temperature curves of model and experiment, the RMSE of temperature at the center point of large surface was 5.09 °C (with a maximum temperature of 212 °C), indicating the accuracy of the model. This paper’s research on arc faults in battery systems revealed the evolution pattern and realized that arcs can trigger thermal runaway in batteries. The model for arc-triggered thermal runaway in batteries is highly accurate, capable of reducing the number of experiments, accelerating experimental progress, and is of significant importance for guiding the design of arc experiments about batteries.