Arc Detection Research Articles

Three-Phase Fault Arc Phase Selection Based on Global Attention Temporal Convolutional Neural Network

For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method based on a global temporal convolutional network. First, this method builds a low-voltage three-phase arc fault data acquisition platform and establishes a dataset. Second, the experimental data were decomposed by variational mode decomposition and analyzed in the time-frequency domain. The decomposed data are reconstructed and used as input to the model. Finally, in order to reduce the fault features lost during the causal convolution operation, the global attention mechanism is used to extract deep fault characterization to identify faults and their differences. The experimental results show that the accuracy of the three-phase arc fault arc phase selection of the model can reach 98.62%, and the accuracy of single-phase fault detection can reach 99.39%. This model can effectively extract three-phase arc fault and phase characteristics. This paper provides a new idea for series fault arc detection and three-phase fault arc phase selection research.

Electrical and non electrical characteristics for the fast detection of high current free-burning arcs

To guarantee save and reliable operation of low voltage grid devices and to protect all devices from disturbance or damage, it is absolutely necessary to separate normal operation from fault state. A typical fault state with an enormous hazard potential is the arc fault. Due to the high level of energy involved, it can lead to the total write-off of affected devices and hence interrupt the energy supply (service). To prevent extensive consequences, there is a high demand to detect these error conditions and to terminate the fault state in a period of a few milliseconds. The analysis of typical electrical and non-electrical signals with numerical algorithms enables fast detection of arc faults.

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.

다항회귀모델의 잔차 정보를 이용한 교류 직렬 아크검출 알고리즘 개발

In this paper, an AC series arc detection algorithm using residual information of a polynomial regression model was developed. The developed algorithm calculates the multiple regression model equation using the given data, and then determines whether there is an arc based on the residual information between the given data and the estimated values calculated by the multiple regression model. The calculation speed was improved by resampling the acquired data to calculate the multiple regression model equation. In order to verify the validity of the proposed method, the mean square error criterion for data with an absolute residual of 0.14 or more was set to 0.1, and as a result of the experiment, the arc and normal data detection performance was 100%, thus verifying the validity of the proposed method.

Detecting Serial Arcs in Aeronautical Applications Using Inductive Sensors

There is a clear trend in the aircraft industry to use more electrical systems in propulsion and electromechanical and electrohydraulic actuators to comply with environmental sustainability and to improve reliability and maintenance processes. The increase of electrical consumption requires an increase of the rated voltages to supply power to these so called More Electrical Aircraft (MEA). Unfortunately, this voltage rise, currently up to 540 Vdc, can lead to ionization processes within the electric wiring due to the lower air density at high altitudes. As a consequence, a degraded insulation can create extremely hazardous events in flight such as arcs between wires (serial) or between wires and fuselage (parallel). Serial arcs in DC bus circuits are specially dangerous for the aircraft operation, so its detection within fractions of seconds is required to ensure a reliable operation. However, during a sustained serial arc the rated current can be passing through air deceiving the protections and avoiding their tripping. In this work, it has been found that arcing introduces high frequency current pulses superimposed to the DC component that can be identified to detect the occurrence of an arc. The frequency components of these pulses depend on the line characteristics and can be detected with inductive sensors. This manuscript also designs and tests a light and inexpensive sensor for arc detection in aircraft applications.

Standard deviation based series DC arc detection method for voltage source converters

Standard deviation based series DC arc detection method for voltage source converters

Series Arc Fault Detection in a Low-Voltage Power System Based on CEEMDAN Decomposition and Sensitive IMF Selection

In the series arc fault detection of a low-voltage distribution network, the features of the fault current signal are easily submerged and arc fault features are difficult to be represented, which greatly increases the difficulty of fault arc detection based on current signals. To solve these problems, a series arc fault detection method combining CEEMDAN decomposition and sensitive IMF selection is proposed. In this paper, the CEEMDAN algorithm is first applied to complete decomposition of the arc current in series faults. Then, 12 feature indicators of the arc current are defined and the frequency band division of the IMF component is realized according to the kurtosis index and energy feature which are more sensitive. The time window-based feature calculation method is proposed to obtain the local features of the time scale of each high-frequency IMF component. Accurate selection of sensitive IMF components is realized by comparing feature indexes such as the variance and root mean square value. Finally, for the current feature set, the second dimension reduction is realized by the subspace transformation algorithm and the series arc fault detection is realized based on the SVM. The actual experiments show that the optimal detection accuracy of the proposed method is 91.67% and the average accuracy of 10 crossvalidation experiments is 88.33%. It shows that the proposed sensitive IMF selection method can effectively capture the fault component signals in the current and the proposed fault feature description method has good representation and discrimination ability.

Solar-cell as trigger element for Ultra– Fast– Earthing Switch (UFES) for low– / medium- voltage; Self – powered

Currently the detection of an internal arc inside a switchgear is done by means of an optical arc flash sensor. This triggers an external power supplied electronic device with charged capacitor to actuate the active primary arc mitigation device. In several cases the arc sensing is linked with the current threshold value in addition. A new and innovative method to detect, trigger and power the arc mitigation device by means of solar-cells which are energized by the arc flash only, has been worked out. There is no need to have the detection of both current and light together because the arc flash itself is used to generate enough power to tigger the active arc mitigation device. The new method using solar-cell triggers and powers the mitigation device even within less than 4…5ms. This paper presents an innovative way how to detect the internal arc in low– and medium– voltage and to trigger and actuate the active arc mitigation device.

Research on the detection of arc fault in series connection of landscape power supply

Aiming at the series fault arc phenomenon in landscape lighting and the hidden dangers of electrical fires, in this paper, a landscape power supply series fault arc model is constructed and its model is simulated. The simulation results show that when a fault occurs, the arc current becomes smaller (almost zero) due to the increase in the impedance of the lighting circuit; this phenomenon is called the “current zero off” phenomenon of the fault arc current. The current zero off phenomenon of the fault arc current is the main fault feature in the landscape lighting circuit. In this paper, the wavelet algorithm is used to detect the fault current waveform. According to the fault characteristics, by judging whether the modulus maximum value of the wavelet coefficient has periodic characteristics with an interval of 100±15 sampling points, it is analyzed whether a series-type arc fault occurs. The built physical model verifies the feasibility and correctness of the arc detection algorithm. The research results of this paper have certain reference value for the detection and application of fault arc.

Detection of Arcing and High Impedance with Electrical Weapons.

Conducted electrical weapons are primarily designed to stop subjects from endangering themselves or others by deploying 2, or more, probes to conduct current via the body to induce motor-nerve mediated muscle contractions, but probe impedance can vary significantly including open circuits from probes failing to complete or maintain a circuit. We tested 10 units of the TASER® 7 model with a range of impedances and open circuit conditions. Pulse data (stored in the device's memory) were used to predict the load resistances and detect arcing conditions. Acoustical data (recorded externally) was evaluated on an exploratory basis as a secondary goal. The average error of predicted resistance, over the physiological load range of 400-1000 Ω, was 8%. Arcing conditions was predicted with an accuracy of 97%. An arcing condition increases the duration of the sound generation. The TASER 7 electronic control device stored pulse-log data for charge and arc voltage yielded forensic analysis of the load resistance with reliable accuracy.

유해 아크 정량화를 위한 스마트 진단 프로그램 구현

Hazardous arc generated by electrical faults are detected for safety accidents in power facilities. Arc detection has a different measurement method depending on the faults. the arc flash from short circuit or overload is analyzed by voltage and current for diagnosis. However, there is a disadvantage in detecting the arc because of sensitivity to noise before it occur. In addition, detection method based on the photosensor may cause an malfunction by the light around environment. The photosensor with 220 nm can prevent malfunction because it not work in sun of the light. The arc size according to the detection speed is shown as the arc duration. This method reduces error according to the detection speed. However, it is necessary to implement a program capable of determining detection and accuracy determination for quantitative measurement. In this paper, we propose a detection method and program with improved detection speed to detect the arc duration of 10 μs. the error of program is compared with the arc duration generated in real time. In the future, we propose that customized application will be possible according to the factors of arc generation in the electric power field.

RECONSTRUCTION OF BRIDGE SUPERSTRUCTURES FROM AIRBORNE LASER SCANNING POINT CLOUDS

Abstract. The paper deals with the 3D reconstruction of bridges from Airborne Laser Scanning point clouds and cadastral footprints. The generated realistic 3D objects can be used to enhance city models. While other studies have focused on bridge decks to fill gaps in digital elevation models, this paper focuses on the decomposition of superstructures into construction elements such as pylons, cables and arches. For this purpose, the bridge type is classified, and a combination of model-based and data-based methods is used that are built on the detection of arcs, catenaries, and line segments in the point clouds. The described techniques were successfully applied to create 3D models of the Rhine bridges in the German state of North Rhine-Westphalia.

시간 및 주파수 영역에서 교류 전력선로상 직렬 및 병렬 교류 아크검출기법들에 대한 연구

In this paper, magnitude and shape of the current flowing on AC line was analyzed when series AC arc and parallel AC arc occur. It was confirmed through experiment that when series AC arc occurs on the AC line, the shape of the current changes more than the magnitude of the current. In addition, in the case of the parallel AC arc, it was confirmed from the experiment that shouldering effect occurred and the line current flowing on the AC line had a distorted current waveform comprising multiple harmonic components, instead of a sinusoidal waveform. To detect series AC arc and parallel AC arc, Zero crossing period (ZCP) and Fast Fourier Transform (FFT) result were used. As a result of analyzing the arc detection capability when the series AC arc and the parallel AC arc occur, in the case of series AC arc, arc detection in time-domain was more effective than in the frequency-domain when the line current was small and detection capability was similar at each domain when the line current was large. In the case of the parallel AC arc, it was confirmed from the experiment that the parallel AC arc was more easily detected than the series AC arc, because the parallel AC arc generates a more distinct current change comparing to the series AC arc.

DC AFCI 성능평가를 위한 UL1699B 표준에 따른 시험설비 구축

This paper deals with the implementation of test facility for DC AFCI(Arc fault circuit interrupter) series arc detection performance evaluation based on UL1699B standard. The implemented test facility consists of DC source, decoupling network, line impedance, inverter and arc generator. The developed electric arc generator enables precise control for spacing between the arc electrodes. Implemented test facility complies with a single full string model presented on UL1699B. Mode 1 to Mode 4 are presented as test modes. The test conditions of inverter voltage, current, minimum arc current conditions, electrode separation speed and distance are satisfied by implemented test facility when arcing occurred in all test modes. Also the series arc detection performance evaluation result of DC AFCI under the mode 1 condition is presented. The implemented test facility is expected to be applied to the series arc detection performance evaluation of DC AFCI under all test mode conditions.

Deep learning-based series AC arc detection algorithms

Deep learning-based series AC arc detection algorithms

Series Arc Fault Detection Based on Random Forest and Deep Neural Network

Series arc is prone to cause fire accidents, but its occurrences induced by different load types and connections make the detection challengeable. This paper proposes a series arc fault detection and location algorithm for multi-load circuit topology, especially for branch arc faults and nonlinear power loads. Several typical loads of paralleling connected are considered to measure the current changes caused by the arcing phenomenon at different positions. Different aspects of arc features are extracted by time-domain, frequency-domain, and wavelet packet energy analysis. A feature selection method based on random forest (RF) is adopted to determine the specific feature sets according to the reduction of Gini impurity. The integrated top ten features with a high correlation to the arc were selected for different combinations of loads and are input into the deep neural network (DNN) for calculating and training. Eventually, a comprehensive arc detection model with the function of detection and location determination for different load types is obtained. It proves that the proposed RF-DNN-based arc detection algorithm can identify and protect the series arc faults in multi-load scenarios efficiently and accurately to meet the practical needs.

전기설비에서 전압변동을 이용한 직렬아크 검출방법

According to the Fire Statistical Yearbook, Electric arcs, such as ignition sources, are a high proportion in Korean fire accidents. Because it is important to detect the arcs prior to the fire accidents, sensors and frequency analysis have been studied to detect them. The arc detection techniques based on the sensors and frequency analysis are limited in location search and reliability in arc detection, respectively. Therefore, it is necessary to study an improved technique for detecting the arc. This paper proposes an arc detection technique that can improve the location search and the low reliability in the arc detection using the voltage fluctuations. The experiments were conducted to obtain the voltage fluctuations from the measured voltage waveforms under conditions with or without an arc for the loads defined in IEC 62606, thereby checking the presence of the arc. We confirmed that the proposed arc detection technique can detect a series arc in electric equipment.

ArcNet: Series AC Arc Fault Detection Based on Raw Current and Convolutional Neural Network

AC series arc is dangerous and can cause serious electric fire hazards and property damage. This article proposed a convolutional neural network -based arc detection model named ArcNet. The database of this research is collected from eight different types of loads according to IEC62606 standard. The two most common types of arcs, including arcs from a loose connection of cables and those caused by the failure of the insulation, are generated in testing and included in the database. Using the database of raw current, experimental results indicate ArcNet can achieve a maximum of 99.47% arc detection accuracy at 10 kHz sampling rate. The model is also implemented in Raspberry Pi 3B for classification accuracy. A tradeoff study between the arc detection accuracy and model runtime has been conducted. The proposed ArcNet obtained an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for real-time processing.

Design of Multi-Information Fusion Based Intelligent Electrical Fire Detection System for Green Buildings

Building management systems are costly for small- to medium-sized buildings. A massive volume of data is collected on different building contexts by the Internet of Things (IoT), which is then further monitored. This intelligence is integrated into building management systems (BMSs) for energy consumption management in a cost-effective manner. Electric fire safety is paramount in buildings, especially in hospitals. Facility managers focus on fire protection strategies and identify where system upgrades are needed to maintain existing technologies. Furthermore, BMSs in hospitals should minimize patient disruption and be immune to nuisance alarms. This paper proposes an intelligent detection technology for electric fires based on multi-information fusion for green buildings. The system model was established by using fuzzy logic reasoning. The extracted multi-information fusion was used to detect the arc fault, which often causes electrical fires in the low-voltage distribution system of green buildings. The reliability of the established multi-information fusion model was verified by simulation. Using fuzzy logic reasoning and the membership function in fuzzy set theory to solve the uncertain relationship between faults and symptoms is a widely applied method. In order to realize the early prediction and precise diagnosis of faults, a fuzzy reasoning system was applied to analyze the arcs causing electrical fires in the lines. In order to accurately identify the fault arcs that easily cause electrical fires in low-voltage distribution systems for building management, this paper introduces in detail a fault identification method based on multi-information fusion, which can consolidate the complementary advantages of different types of judgment. The results demonstrate that the multi-information fusion method reduces the deficiency of a single criterion in fault arc detection and prevents electrical fires in green buildings more comprehensively and accurately. For the real-time dataset, the data results are presented, showing disagreements among the testing methods.

Research on Deviation Detection of Belt Conveyor Based on Inspection Robot and Deep Learning

The deviation of the conveyor belt is a common failure that affects the safe operation of the belt conveyor. In this paper, a deviation detection method of the belt conveyor based on inspection robot and deep learning is proposed to detect the deviation at its any position. Firstly, the inspection robot captures the image and the region of interest (ROI) containing the conveyor belt edge and the exposed idler is extracted by the optimized MobileNet SSD (OM-SSD). Secondly, Hough line transform algorithm is used to detect the conveyor belt edge, and an elliptical arc detection algorithm based on template matching is proposed to detect the idler outer edge. Finally, a geometric correction algorithm based on homography transformation is proposed to correct the coordinates of the detected edge points, and the deviation degree (DD) of the conveyor belt is estimated based on the corrected coordinates. The experimental results show that the proposed method can detect the deviation of the conveyor belt continuously with an RMSE of 3.7 mm, an MAE of 4.4 mm, and an average time consumption of 135.5 ms. It improves the monitoring range, detection accuracy, reliability, robustness, and real-time performance of the deviation detection of the belt conveyor.