Series Arc Fault Research Articles

Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

In this work, an in-depth analysis providing the optimal parameters estimation for discrete wavelet transform (DWT) applied to detection of series arc faults in the household AC power network is presented. The influence of three parameters was investigated: the choice of mother wavelet, level of decomposition and sampling frequency. The line current was used as input for all analyses. A performance criterion based on the energy computation of line currents with and without arc faults was defined and used to compare the influence of 550 combinations of these three parameters on the arc fault detection performances for different loads, including two household appliances. The study showed that the right choice of these three parameters greatly influences the arc fault detection performances. Moreover, for each tested load a frequency range providing maximal arc fault detection performances is identified. The study showed also that the choice of the mother wavelet is less critical than the two other parameters.

The Detection of Series Arc Fault in Photovoltaic Systems Based on the Arc Current Entropy

This paper presents a series arc-fault detection algorithm for photovoltaic (PV) systems, which relies on the quantum probability model theory. The algorithm determines the presence of the arc by calculating the modified Tsallis entropy of the PV panel current. Based on the calculated entropy, the algorithm is able to differentiate an arc state (when the current variations are chaotic) from a no-arc state (when the current variations are ordered). The proposed algorithm enables arc-fault detection on a plug-and-play principle, requiring no prior information about the PV system in which it operates. The operation of the algorithm was first simulated on the prerecorded data from the system with the PV panel simulator and the commercial PV inverter. A laboratory prototype of the detector was then built and tested in a real 1.6-kW grid-connected PV system with different PV inverter, without any additional parameter adjustments. Both the sensitivity and the robustness of the detector were confirmed. The tests have shown that both the sustained series arcs and the small sparking series arcs were successfully detected, and there was no false detection due to the MPP tracker operation, PV current step change, or inverter turn on transient.

Shunt active power filter-based approach for arc fault detection

In this paper, we present a novel method based on a single-phase active power filter (APF) for series arc faults detection in an AC electrical installation. The APF's reference current is used as the starting point for our method tested on a large variety of loads: resistors, vacuum cleaner, rotary drill, dimmer and AC-DC power supplies. Firstly, the proposed method is validated at the simulation level using the Matlab software and then experimentally using the Hardware-In-the-Loop (HIL) approach with an FPGA Altera Stratix III prototyping board. The results obtained in this work show that series arc faults can be successfully detected with an APF, only by updating its digital control with the arc fault detection functionality, instead of designing from the very beginning an arc-fault detection specific device.

Detection of serial arc fault on low-voltage indoor power lines by using radial basis function neural network

Dangerous serial electric arc faults on low voltage power lines must be detected before fire hazards occur. The detection technology is requested to have high accuracy. However, the characteristics of line current waveform during serial arc faults are complicated. This paper uses the approach of a radial basis function neural network (DRBFNN) to identify the occurrence of serial arc faults. At first, the discrete wavelet transform (DWT) is employed to obtain the time–frequency domain characteristics of line current waveforms to reflect the serial arc fault patterns. Then some measured data are used to train the DRBFNN. Finally, this study compares the detection results under different loading conditions and operation conditions. It also compares the detection results with other two methods, detection of sub-spectrum energy (DSE) and high frequency detection by wavelet transform (HFDWT). It can be observed that DRBFNN has better ability than DSE and HFDWT to detect serial arc faults.

A Series Arc Fault Detection Strategy for Single-Phase Boost PFC Rectifiers

This paper proposes a series arc fault detection algorithm which incorporates peak voltage and harmonic current detectors for single-phase boost power factor correction (PFC) rectifiers. The series arc fault model is also proposed to analyze the phenomenon of the arc fault and detection algorithm. For arc detection, the virtual dq transformation is utilized to detect the peak input voltage. In addition, multiple combinations of low- and high-pass filters are applied to extract the specific harmonic components which show the characteristics of the series arc fault conditions. The proposed model and the arc detection method are experimentally verified through a boost PFC rectifier prototype operating under the grid-tied condition with an artificial arc generator manufactured under the guidelines for the Underwriters Laboratories (UL) 1699 standard.

Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis

Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series arc faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series arc fault features. The phase information of arc fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing arc faults. Finally, least squares support vector machine is used to accurately identify series arc faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining arc fault detection rate above 97%.

Novel detection method for DC series arc faults by using morphological filtering

It has been known that DC arc faults pose a hazard in power electronics systems. Due to the fault current is lower than normal current load, series arc is more difficult to be discovered than parallel arc. The traditional methods of DC series arc faults detection have some disadvantages, such as large computation, long delay and easier influence by interferences. In this paper, a novel method with multi-scale morphological filtering was utilized for the fault detection of DC series arc. Compared to the tradition method, the proposed method is more simple and efficient. It was verified that it was convenient for field application of on-line monitoring and diagnosis with a good ability to prevent misjudgments from the environment interference.

Research on Low-voltage Series Arc Fault Detection Method Based on Least Squares Support Vector Machine

Arc fault is one of the important reasons of electrical fires. In virtue of cross talk, randomness and weakness of series arc faults in low-voltage circuits, very few of techniques have been well used to protect loads from series arc faults. Thus, a novel detection method based on support vector machine is developed in this paper. If series arc fault occurs, high frequency signal energy in circuit will increase a lot, and current cycle integrals are variable and erratic. However, high frequency signal energy will be influenced by cross talk in a nearby branch circuit. Besides, current cycle integrals will also vary while the working states of circuit changed. To better describe series arc faults, two characteristics include high frequency signal energy and current integral difference are extracted as support vectors. Based on these support vectors, least squares support vector machine is used to distinguish series arc faults from normal working states. The validity of the developed method is verified via an arc fault experimental platform set up. The results show that series arc faults are well detected based on the developed method.

Development of Fault Detector for Series Arc Fault in Low Voltage DC Distribution System using Wavelet Singular Value Decomposition and State Diagram

It is well known that series arc faults in Low Voltage DC (LVDC) distribution system occur at unintended points of discontinuity within an electrical circuit. These faults can make circuit breakers not respond timely due to low fault current. It, therefore, is needed to detect the series fault for protecting circuits from electrical fires. This paper proposes a novel scheme to detect the series arc fault using Wavelet Singular Value Decomposition (WSVD) and state diagram. In this paper, the fault detector developed is designed by using three criterion factors based on the RMS value of Singular value of Approximation (SA), Sum of the absolute value of Detail (SD), and state diagram. LVDC distribution system including AC/DC and DC/DC converter is modeled to verify the proposed scheme using ElectroMagnetic Transient Program (EMTP) software. EMTP/MODELS is also utilized to implement the series arc model and WSVD. Simulation results according to various conditions clearly show the effectiveness of the proposed scheme.

Ignition Characteristics of Residential Series Arc Faults in 220-V HIV Wires

Unintentional arc faults due to damaged power line cords and loose connections can cause serious fires; thus, they are a safety hazard in residential areas. Therefore, arc fault circuit interrupters are being increasingly used in homes to protect against arcs that may cause fires. In this paper, we analyzed the ignition characteristics of series arcs on the basis of load current. Furthermore, we recommended suitable arc clearing times through characteristics analysis in the Korean residential electric environment. We determined the arc clearing time via the carbonized path arc clearing time test using an ultraviolet signal detecting system according to the test method of the UL 1699 standard. In the experiments, heat-resistant indoor PVC insulated (HIV) wires, which is a type of residential electrical wire, were used as the wire specimens.

Smart detection technology of serial arc fault on low-voltage indoor power lines

Abstract This paper employs the discrete wavelet transform (DWT) and an artificial neural network to identify the occurrence of serial arc faults on indoor low voltage power lines. Electric arc faults on power lines must be detected in order to turn off the electric power sources before fire events occur. However, since the characteristics of line current waveforms during serial arc faults are complicated, smart detection technology is required to have high accurate recognition. The DWT is utilized to obtain the time-domain characteristics of line current waveforms, and the signal energy of some sub-bands is useful information to reflect the serial arc fault patterns. And then, a radial basis function neural network (RBFNN) is trained by using the data of signal energy obtained from DWT. After the training process, the RBFNN has excellent ability to identify the serial arc-fault conditions. At last, the accumulative RBFNN outputs of 30 power cycle line current data are used to certify the occurring of a serial arc fault on the line. This study also compares the results of detecting serial arc faults with a commercial arc-fault circuit interrupter (AFCI) to reveal the goodness of the purposed method.

Identification of Low Voltage AC Series Arc Faults by using Kalman Filtering Algorithm

Arc faults often occur in residential low voltage AC supply environment. It is dangerous because the sparks accompanying with arc faults may lead to electric fire even safety hazard. Effective methods of arc faults diagnosis in circuits are essential for safety interrupters in household. A novel method for low voltage AC series arc faults identification based on time-domain feature extraction by using Kalman filtering algorithm is proposed in this paper. This method monitors the instantaneous value of series current in circuits, calculates an elaborate pre-designed eigenvalue each power cycle by using Kalman filter with acquired current samples, and compares the eigenvalue with a reference value. Once the deviation between them exceeded a predefined threshold in continuous eight power cycles, the method reports an arc fault. Experimental research was studied in laboratory with typical electrical loads using the proposed arc faults identification method. Results show that it is applicable to detect arc faults within eight power cycles. The response time of the method could fit the minimum requirements of the standard UL1699. DOI: http://dx.doi.org/10.5755/j01.eee.20.5.4294

Research on Low-Voltage Series Arc Fault Detection Based on Higher-Order Cumulants

Arc faults are one of the main reasons of electrical fires. For the difficulty of series arc fault detection, very few of techniques have successfully protected loads from arc faults in low-voltage circuits, especially in China. When series arc faults occur, shoulders will appear in load currents. The shoulder widths of non-arc faults such as normal load arcs are stable, while those of arc faults are variable because the appearance of arc faults is erratic. Therefore, a novel detection method based on shoulder characteristics was proposed. To better capture shoulder widths, original currents were firstly converted into pulses. Then, the pulse widths were used to detect arcs and the fourth-order cumulants of their differential were used to distinguish arc faults from normal operations. Finally, an arc fault detection device (AFDD) prototype was developed for test. The results show this prototype can discriminate arc faults effectively from normal operations.

Signal Detection Technology for Low Voltage Arc Fault Circuit Interrupter Using FPGA

This paper is studying the low voltage arc fault circuit interrupters (AFCI) based on FPGA Signal detection. The arc fault generator is designed to generate artificial low voltage series arc faults. The AFCI signal detection circuits are tested by using the measured voltage and current waveforms from the testing circuits. It is expect to develop high performance signal detection circuits and the key component using in AFCI. Finally, the experimental data with serial arc faults are used to test the detecting methods and compare with the commercial devices. The purposed detecting methods can effectively detect the occurring of series arc faults, and the probability of malfunction is low.

Identification Method of Low-Voltage Series Arc Fault Based on Harmonic Component Relative Change Coefficient

Low voltage series arc fault is one of the main reasons caused the accident of electrical fire, so it is necessary to detect and identify the fault arc. The low voltage series arc fault is researched in this paper, current waves of some typical electric equipment under arc fault and normal operation are measured, and DFT transform of current waveforms are done by Matlab software, so that an identification method of low-voltage series arc fault based on the harmonic component relative change coefficient is presented in this paper. The simulation results show that this method can effectively identify whether the arc fault occur.

GIC 적용 대역통과필터를 이용한 컴퓨터 부하의 직렬 아크고장 전류 우수고조파 분석

In this paper, the even harmonic current(2nd, 4th, 6th, 8th) of the arc fault current and normal current were measured in computer load and analyzed. The BPF with GIC was developed to detection of harmonic, the exact center frequency and a high degree of sharpness could be easily obtained. The total even harmonic distortion due to series arc fault in computer load was 31.2%, this value was increased 3.9 times better than the total even harmonic distortion of normal current. The results of analysis of arc fault current RMS variation rate and Peak variation rate per half-cycle, The RMS average variation rate are as follows ; the 2nd harmonic was 0.24, the 4th harmonic was 0.15, the 6th harmonic was 0.19, the 8th harmonic was 0.25, respectively. The Peak average variation rate are as follows ; the 2nd harmonic was 0.19, the 4th harmonic was 0.12, the 6th harmonic was 0.13, the 8th harmonic was 0.15, respectively. The results of this analysis utilize data to detect of series arc fault on wiring of computer load.

Identifying causes for certain types of electrically initiated fires in residential circuits

AbstractWhile there are many circumstances that can lead to residential electrical fires, it is useful and practical to identify and document potential hazardous conditions and provide physical explanations of electrically initiated fires. Failure modes for a variety of parallel and series arc faults are described along with circuit breaker response, electrical data, photos, and video frames to explain the conditions that led to a hazardous condition. This work identifies some of the various conditions that can create overheated hazardous conditions and shows how wire insulation is commonly the first to ignite. Conditions explored include bundled extension cords under rugs, abused NM‐B at a load center entrance, broken wires and loose connections. Physical phenomena investigated include glowing connections, series and parallel arcing faults. Low and high current arc energies are discussed and compared to the combustible energy of volatile gases produced from wire insulation to illustrate the wide difference in the arc energy compared to chemical combustion energy from the decomposed PVC. Electrical wire insulation properties were also measured on various wire types to show the effects of thermal aging. Hardness of the wire insulation and cracking of the insulation was used as a measure of insulation lifetime at 140∘C. Copyright © 2010 John Wiley & Sons, Ltd.

아크고장 검출기술의 개발

2007년도에 9,128건의 화재가 전기적인 원인에 의해서 발생되었고, 이들 화재에 의해 사망 29명과 부상 262명의 인명피해가 있었다. 아크고장은 전기화재의 주요 원인의 하나로, 일반적으로 높은 열을 발생시키며 쉽게 주변의 인화성물질을 점화시킨다. 그러나 기존의 차단기는 과전류, 단락전류, 누설전류에 대해서만 동작하기 때문에, 이에 대해서는 현재 특별한 보호책이 없는 실정이다. 본 논문은 직렬아크 고장전류에 의한 전선의 발화특성에 대한 실험에서 얻어진 결과를 통해 UL에서 제안하고 있는 기준이 국내의 현실에 적절하지 않음을 제시하였고, 이 결과를 기초로 개발된 기술의 동작특성 실험을 실시하여 아크고장회로를 보호할 수 있는지에 대한 신뢰성을 확인하였다. 모의실험을 통한 동작특성 분석결과, 개발된 아크고장 검출 기술을 적용하면 전기화재의 주요원인인 아크고장에 의한 화재를 현저히 저감시킬 수 있을 것으로 판단된다. In 2007, 9,128 fires were actually caused by electrical faults and these fires resulted in 29 deaths and 262 injuries. Arc-faults were one of the major causes of these fires. When an unintended arc-fault occurs, it generates intense heat that can easily ignite surrounding combustibles. But, because conventional circuit breakers only respond to overloads, short circuits, and leakage currents, the breakers do not protect against arcing conditions. This paper presents results obtained in experiments on ignition behavior of wire by series arc fault currents and techniques developed to detect the arc-faults. The developed technique was tested after installation to make sure that they are working properly and protecting the circuit. If the developed arc fault detection technique is applied, the electrical fires caused by an arc-fault can be reduced.

Arc fault detection scheme for 42-V automotive DC networks using current shunt

Introduction of higher dc system voltage distribution networks, such as the 42-V PowerNet in future passenger vehicles appears to be an unavoidable consequence of meeting the increasing future electrical power demand. Higher voltage electrical distribution networks in vehicles force considerable component and system changes regarding electrical safety and reliability. In the event of an arc fault, e.g., when a wire is pinched or cut, or disengaged terminals under load etc., the resulting current may be significantly lower than the trip current of the protection devices such as fuses and circuit breakers. In these cases either the fault is cleared late (depending on the time/current characteristics of the fuse) or, in some cases the fault may not be cleared at all. A cost effective arc fault detection scheme using input side current shunt was developed, built, and tested with different loads including motor loads to clear both parallel and series arc faults in a 42-V dc network. This paper presents the details of the developed arc fault detection scheme and test results under several fault conditions