Arc Fault Detection Devices Research Articles

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.

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%.

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.

Lightweight Arc Fault Detection Method Based on Adam-Optimized Neural Network and Hardware Feature Algorithm

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

Detection performance of arcing fault detectors in the Chinese market according to the Chinese Standard GB/T 31143

There are two sets of standards available in the Chinese market that specify the design requirements and operating tests for arc fault detection devices (GB/T 31143) and arcing fault detectors (GB 14287.4) respectively. While arcing fault detectors (AFDs) are claimed to have passed GB 14287.4 standard tests, it remains unclear whether these AFDs can pass GB/T 31143 standards and effectively detect arc fault signals. This study experiments to explore the operational performance of AFDs in the Chinese market based on GB/T 31143 standards, revealing that not all AFDs can pass this test and provide adequate protection for people’s lives and property. The main reason for the failure is that the arcing characteristics, such as flat shoulders and reduced amplitude, of the signal are easily masked by other branch circuit loads.

A method for determining arc breakdown in low-voltage electrical networks

In low voltage distribution networks, there is a high risk of fires caused by wiring faults that lead to short circuits. Arcing protection devices (ARCs) already exist, but experiments show that they also often do not disable such faults. The operation of most existing arc fault detection devices is based on the detection of high frequency current components and their comparison with load profiles. For the fast arc fault recognition low-frequency oscillations can be assessed, which serve as a marker of the arc fault presence in a LV distribution network. This method of arc fault recognition in LV networks is easier to implement practically. Using the windowed Fourier transform or Wavelet analysis, it is possible to determine the change in time of the harmonic components of the current, which will be a characteristic sign of the presence of an arc fault along with other signs.

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.

스마트 아크차단기(AFDD)와 IoT 플랫폼을 활용한 화재 조사기법 연구

According to the data for the last five years, about 33.7% of the total fire causes were electricity, of which more than 81% was caused by arcs. Currently, in Korea, MCCB(Molded Case Circuit Breaker) and ELB(Earth Leakage Breaker) are legally required to be installed on distribution boards, but these do not have an arc detection function, so it is required an arc detection and blocking device, AFDD(Arc Fault Detection Device). Furthermore, in this study, data results were calculated through an arc wavelength analysis experiment of a smart AFDD(Arc Fault Detection Device) using IoT technology. If these results are continuously accumulated and data is constructed, it can be used not only as fire prevention but also as an accurate and objective point of the fire occurrence and as an identification criterion for the cause of the fire occurrence.

Arc fault detection devices efficiency in cooperation with switch-mode power supplies

The paper presents a special case of ineffective protection with Arc Fault Detection Device. The purpose of this article was to investigate why an AFDD does not detect sparking in a circuit supplying a switch-mode power supply. Therefore, laboratory tests according to IEC 62,606 and tests under real operating conditions were performed for this purpose. The devices of different manufacturers producers were compared with each other and it was checked if a similar problem is repeated with each AFDD. The tests conclude that switching-mode power supplies as capacitive loads negatively influence the spark detection efficiency, the selection of an AFDD should always be preceded by an analysis of the minimum operating currents (2.5 A) and there are differences in the detection sensitivity between devices of various manufactures.

Series DC Arc Fault Detection Method for PV Systems Employing Differential Power Processing Structure

Arc fault detection is an important process for ensuring the safety of PV and grid-connected inverters and is essential for producing PV systems in real applications. However, it is difficult to detect series dc arc faults using conventional fuses because these faults produce only small current variations. In addition, the arc fault detection devices have limited performance, because they focused on arc fault detection between PV arrays and inverter. Moreover, they require the additional current and voltage sensors to implement the arc fault detection algorithm. This article proposes an arc fault detection algorithm employing differential power processing (DPP) structure. The proposed algorithm only uses intrinsic voltage sensors of DPP and inverter, which can improve the cost-effectiveness of PV systems. In addition, the proposed algorithm can integrate the functionality of maximum power processing for each PV panel and arc fault detection. The voltage relationship between DPP and inverter is analyzed to detect the arc fault condition according to the DPP voltage sensing type. The performance of DPP units and its voltage relationship are verified with the control hardware-in-the-loop system. The arc fault detection performance is verified with the prototype PV system.

Phase Analysis of Series Arc Signals for Low-Voltage Electrical Devices

An arc fault is an electrical breakdown of the insulating medium in an electrical system. When arc faults occur, they cause electrical fires with local sparks and temperatures of over 5000 °C. A series arc is generated in series with a load due to an incomplete connection between cords or a loose connection between a cord and terminal. However, it is hard to detect series arc faults with arc protection devices because the fault current flowing by series arc in the circuit is not higher than the load current. Especially in Korea, preventing electrical fires by series arc is rarely applied because there is no national standard for arc fault circuit interrupters (AFCI) and arc fault detection devices (AFDD). Therefore, many studies for reliable arc detection and analysis are still necessary to prevent electrical fires. In this study, phase analyses of series arc signals for low-voltage electrical devices such as heaters, computers, refrigerators, and air conditioners were conducted. The arc generator was fabricated according to UL 6199 and an optimal filter was designed to detect series arc signals without any attenuation. The phase of detected series arc signals was analyzed according to load types and finally a new algorithm was proposed based on the result of phase-resolved series arc (PRSA) analysis to identify types of loads.

Testing System for the On-Site Checking of Magneto-Thermal Switches with Arc Fault Detection

Arcing is a harmful condition that can lead to electrical fires. The U.S. National Electrical Code requires the installation of electric arc breakers in all residential areas. However, such devices can fail, and therefore, may not work when needed. At present, there are no tests to confirm the correct operation of this type of device. This study aimed to propose an experimental test method to verify the proper functioning of magneto-thermal switches with an electric arc protection function for the occurrence of arc faults. The results show that the proposed method adequately detected the correct operations and tripping time by using different surge suppressors. The proposed system lays the foundations for a portable test system for the periodic checking of electrical systems in which arc fault detection devices (AFDDs) are installed.

Arc Fault Protection and Detection

Arc faults in electric circuits are recognized as an important cause of fire. The first Arc Fault Circuit Interrupter (AFCI) has been patented in 1980 in the United States. AFCI is a device designed to detect electric arc faults which was prescribed for use by the National Electric Code (NEC, US Wiring Regulation) in January 2008. The NEC describes it as ‘A device intended to provide protection from the effects of arc faults by recognizing characteristics unique to arcing and by functioning to de-energize the circuit when an arc fault is detected’. At the beginning of 2012 the Arc Fault Detection Device (AFDD) began to be introduced into the IEC world, culminating in the publication of Technical Product Standards IEC 62606 ' in August 2013, which sets out the requirements for arc fault protection devices. This paper underlines the importance of preventing electrical fires by using the new technology of AFDDs, which largely extend the protection offered by traditional circuit breakers like MCBs and RCDs. Product standard IEC 62606 and functional tests are reviewed, the functioning of AFDDs is explained and a method to design robust algorithms for arc fault detection is proposed.

DC Series Arc Fault Detection Algorithm for Distributed Energy Resources Using Arc Fault Impedance Modeling

Arc fault detection is important technology to guarantee the safety of power systems and is therefore essential for producing practical power systems for real-world applications. However, fuses and arc fault detection devices (AFDD) struggle to detect series arc faults in DC systems, because the series arc fault induces small current variation between the normal and abnormal conditions. In addition, switching noise from the grid-connected inverter makes detecting arc fault conditions even more difficult. This paper proposes arc fault detection algorithm based on the relative comparison of current variability in terms of frequency spectrum and time series. The operational principle of the proposed algorithm is analyzed to detect the arc fault condition. In addition, the investigation of arc fault impedance using the small-signal modeling can obtain the resonant frequency of arc fault condition at low frequency range. From the impedance model, the frequency analysis range can be designed to avoid the switching noise of inverter. The performance of proposed arc fault detection algorithm is verified with a 3.8 kW grid-connected PV system and arc fault generator.

Time-Current Tripping Curves of Arc Fault Detection Devices

Time-Current Tripping Curves of Arc Fault Detection Devices

Measures to Minimize Series Faults in Electrical Cords and Extension Cords

In electrical power systems, cords and extension cords are exposed to mechanical damage and other insulation stresses. Mechanical damage to stranded conductors can reduce locally their cross section or break them and cause anomalous local conditions of overheating or arcing. The ordinary protective devices cannot detect the series faults that persist, so the fault point remains energized and is subject to electric shock and fire hazards. Effective protection can be accomplished by implementing active and passive measures: installing arc-fault circuit interrupters or arc-fault detection devices, able to detect arcing faults, or wiring the circuits with a grounding protection conductor to involve the ground in every fault. In this way, residual current protective devices (residual current devices or ground fault protective devices) quickly protect the series faults not only with arc, but also without it. Ground-fault-forced cables facilitate by design the conversion of any kind of cable fault to a ground fault. They are particularly recommended for cords and extension cords, internal circuits to grounded equipment, uninterruptible power system continuity circuits, aircraft circuits, road tunnels, data centers, refrigerated containers parks, residences, and hospitals.

Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

Commercial arc fault detection devices in military electromagnetic environment

Arc fault detection device (AFDD) is the latest protection device for low voltage circuits. It is used to detect arcing faults that cannot be detected by circuit breakers or residual current devices. Arcing faults are one of the main causes for electrical fires. Arc fault detection device is an active electronic device which operation is typically based on measurement of low and high frequency characteristics of the current passing through. Based on measured current characteristics and the knowledge of the arcing unique deviations in current, arc fault detection device detects possible arcing and disconnects the protected circuit. Arc fault detection device as an active electronic device is a potential source and victim of electromagnetic interference. Electromagnetic compatibility requirements are especially very demanding in military applications where low emission levels and high immunity levels are required. This article presents test results from electromagnetic compatibility testing of five different commercial arc fault detection devices against military standard requirements and analyses the suitability of the arc fault detection devices in military electromagnetic environment.

Practical papers, articles and application notes

Arcing faults, if not detected early, can lead to electrical fires. An Arc Fault Detection Device (AFDD) aims to detect arcing faults that cannot be detected by circuit breakers or residual current devices. AFDD is an active electronic device that measures low and high frequency characteristics of the current passing through it. AFDD can be a potential source and victim of EMI, especially in a military electromagnetic environment, where low emission levels and high immunity levels are necessary. Janne P. Pulkkinen from Finnish Defence Forces, Finland, discusses the EMC requirements of AFDD in the first paper, "Commercial Arc Fault Detection Devices in Military Electromagnetic Environment".

Study on Measuring Method and Experiment of Arc Fault Detection Device

Arc fault is one of the main inducement of electric fires. Arc Fault Detection Device (AFDD) can detect arc fault effectively in electric fires. Arc fault detection and unhooking standard of AFDD are the key to practical application. In the paper, an arc fault continuous production system was developed, which could count the arc half wave number. Then, combining with the UL1699 standard, ignition probability curve of cotton and unhooking time of various currents intensity were obtained by experiments. The total energy of combustion arcing could be expressed effectively by area of arc current curve. Experiments proved that electric fires would be misjudged or missed only using arc half wave number as AFDD unhooking basis. A self-developed AFDD was tested . The result showed that total protection time of AFDD was the sum of arc half wave cycling number, arc wave identification time and unhooking mechanical operation time. The unhooking mechanical operation time is longest. The total protection time of AFDD depended on the shortest mechanical operation time.