Arc Detection Research Articles

A novel method for detecting the pantograph–catenary arc based on the arc sound characteristics

In recent years, the speed and the current transmission density of high-speed railways are increased. As a result, the rate of the occurrence of pantograph–catenary arc also increases, which directly influences the service performance of the pantograph–catenary system. The detection and identification of the pantograph–catenary arc is of great significance for evaluating the off-line level to ensure the safe operation of the pantograph–catenary system. The currently used conventional optical arc detection method would be distorted by strong sunlight or other environmental lights in the actual operation, which would affect the accuracy of detection. On the contrary, the arc sound signal is not affected by the diurnal variation and can be easily obtained; it can also be distinguished well from other interfering sounds. In this paper, a new method that uses the arc sound is proposed to realize the effective detection of the pantograph–catenary arc. The frequency spectrum and the intensity characteristics of the pantograph–catenary arc sound are analyzed. The results show that the frequency band of the arc sound signal is wide and mainly gets distributed at 5–17 kHz. The curve of the sound waveform is drawn by calculating the short-time average energy, which is used to obtain the arc’s starting time, duration, and intensity, for calculating the off-line rate.

Detection and analysis of series arc using non-conventional methods in low-voltage switchboards

Abstract Detection and analysis of series arc in low-voltage switchboards have significant meaning for preventing the electrical fires. However, the conventional current and voltage methods have low a sensitivity to sense the minute arc discharge, leading to the fail operation of arc fault circuit interrupter. Therefore, this paper dealt with the application of non-conventional methods, including the ultra-violet (UV), acoustic emission (AE), and transient earth voltage (TEV) sensor in arc detection, for the purpose of improving the detection sensitivity and reducing the potential electric fires. Three types of typical arc faults in low-voltage switchboards were simulated and the actual detection environment was configured. From the results, the wavelength of UV light emitted from arc was 200–400 nm and the arc-induced AE signal had a frequency range of 40–600 kHz. The TEV signals generated from three types of arc faults presented different frequency spectrums, based on which the time-frequency map was used to classify the fault type.

Observations and modelling of ion cyclotron emission observed in JET plasmas using a sub-harmonic arc detection system during ion cyclotron resonance heating

Measurements are reported of electromagnetic emission close to the cyclotron frequency of energetic ions in JET plasmas heated by waves in the ion cylotron range of frequencies (ICRF). Hydrogen was the majority ion species in all of these plasmas. The measurements were obtained using a sub-harmonic arc detection system in the transmission lines of one of the ICRF antennas. The measured ion cyclotron emission spectra were strongly filtered by the antenna system, and typically contained sub-structure, consisting of sets of peaks with a separation of a few kHz, suggesting the excitation of compressional Alfvén eigenmodes closely spaced in frequency. In most cases the energetic ions can be clearly identified as ICRF wave-accelerated 3He minority ions, although in two pulses the emission may have been produced by energetic 4He ions, originating from third harmonic ICRF wave acceleration. It is proposed that the emission close to the 3He cyclotron frequency was produced by energetic ions of this species undergoing drift orbit excursions to the outer midplane plasma edge. Particle-in-cell and hybrid (kinetic ion, fluid electron) simulations using plasma parameters corresponding to edge plasma conditions in these JET pulses, and energetic particle parameters inferred from the cyclotron resonance location, indicate strong excitation of waves at multiple 3He cyclotron harmonics, including the fundamental, which is identified with the observed emission. These results underline the potential importance of ICE measurements as a method of studying confined fast particles that are strongly suprathermal but have insufficient energies or are not present in sufficient numbers to excite detectable levels of γ-ray emission or other collective instabilities.

Optimal Design of a Band Pass Filter and an Algorithm for Series Arc Detection

Detection and analysis of series arcs is significantly meaningful for preventing arc-caused electrical fires in advance. However, the improvement of arc detection sensitivity and the discrimination of arc conditions are still challenges when developing an arc fault detector. In this paper, arc signals in various loads with three major incomplete connection states were detected and further analyzed using the discrete wavelet transform. It was verified that the db13 was the optimal mother wavelet to analyze the arc pulses and the decomposed signals in the detail components of D5, D6, D7, and D8 were related with arc phenomena. Therefore, a band pass filter with a frequency from 2.4 to 39 kHz was designed, which can extract arc signals while eliminating the AC mains current and noise generated in loads. By investigating the arc signal energy as well as the arc pulse counts that were important parameters of arc occurrence, an arc diagnosis algorithm was developed based on LabVIEW program for electrical fire prevention.

In-Situ Detection Method of Abnormal Plasma Discharge in Plasma-Assisted Deposition Processes

Arc is an abnormal discharge in a plasma-processing chamber that results in high current discharge marks and particles on wafers. However, it is difficult to identify or observe it during an ongoing process. In this study, we report on the observations of plasma arcs during various plasma processes through a non-invasive optical plasma monitoring system devised for the in situ detection of abnormal discharge. The employed optical monitoring based arc detection system provides valuable indications of particle generation resulting from a charge imbalance in the chamber. The devised non-invasive and real-time method can detect such disturbances down to the order of tens of microseconds. Successful monitoring of arcs that appear in various types of plasma-assisted deposition processes are presented in this paper. We also present examples of detected arcs in various plasma processes, such as plasma-enhanced chemical vapor deposition, high density plasma-chemical vapor deposition, amorphous carbon layer deposition, and plasma-enhanced atomic layer deposition. The suggested method plays an important role for the next level of arc-detection research and plasma diagnostics applications.

Detection and Identification of the Series Arc in an Indoor Wiring System

Detection and Identification of the Series Arc in an Indoor Wiring System

Cluster Analysis Based Arc Detection in Pantograph-Catenary System

The pantograph-catenary system, which ensures the transmission of electrical energy, is a critical component of a high-speed electric multiple unit (EMU) train. The pantograph-catenary arc directly affects the power supply quality. The Chinese Railway High-speed (CRH) is equipped with a 6C system to obtain pantograph videos. However, it is difficult to automatically identify the arc image information from the vast amount of videos. This paper proposes an effective approach with which pantograph video can be separated into continuous frame-by-frame images. Because of the interference from the complex operating environment, it is unreasonable to directly use the arc parameters to detect the arc. An environmental segmentation algorithm is developed to eliminate the interference. Time series in the same environment is analyzed via cluster analysis technique (CAT) to find the abnormal points and simplified arc model to find arc events accurately. The proposed approach is tested with real pantograph video and performs well.

Critical arcs detection in influence networks

The influence class of network problems models the propagation of influence (an abstraction of cascading beliefs, behaviors, or physical phenomena) in a network. Such problems have applications in social networks, electrical networks, computer networks, viral spreading, and so on. These types of networks have also been studied through the lens of critical arcs detection; that is, which arcs (edges) are the most important for maintaining some property of the network (e.g., connectivity). We introduce a new class of problems at the intersection of these two models. Specifically, given a set of seed nodes and the linear threshold influence propagation model, our work proposes to determine which arcs (e.g., relationships in a social network or communication pathways in a telecommunication network) are most critical to the influence propagation process. We prove NP‐hardness of the problem. Time‐dependent and time‐independent mixed‐integer programming (MIP) models are introduced. Insights gleaned from MIP solutions leads to the development of an improved MIP‐based exact algorithm rooted in the idea of diffusion expansion. A heuristic based upon a new centrality measure is also proposed, and computational results are presented. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 71(4), 412–431 2018

Method for Online Detection of Arcing in Low-Voltage Distribution Systems

This paper presents an algorithm for online detection of arcing in low-voltage (230 V, 50 Hz) distribution systems. Electromagnetic radiation has been used as a feature for discrimination between arc and nonarc signals. The output of the electromagnetic radiation sensor has been analyzed in the spectral domain using the log-spectral distance metric for the detection of arcing. For validation of this method, a testbench comprising an arc generation setup has been developed in the laboratory. The algorithm has been tested against arc and arc-mimicking normal signals using the testbench to validate its effectiveness. Further, the algorithm is implemented online using xPC target (an embedded platform) for studying the feasibility and accuracy of its online implementation. A system based on this method can continuously monitor the health of a distribution network and, thus, can be very helpful in safeguarding against fire hazards due to electric arcing.

Diamond windows diagnostics for fusion reactors—Updates of the design

The future nuclear fusion power plants will require Electron Cyclotron Heating and Current Drive (ECH&CD) systems to heat up and stabilize the plasma inside the vacuum vessel. One of the key components of such systems is the Chemical Vapor Deposition (CVD) diamond window. The purpose of this device is to act as vacuum and tritium boundary while providing a high microwave transparency with minimal reflectivity. Although suited for high power microwave operation, the windows shall be internally monitored in order to properly ensure the ECH system efficiency and safety.In this paper, the latest assessment study on a set of diagnostics to be part of the window assembly is shown. The required diagnostics include arc and tritium detection, microwave stray radiation (perpendicular to the main beam and generated by cracks in the windows), pressure and disk temperature measurements. The devices must have a compact, simple and flexible layout, with a rugged design, to maximize serviceability and durability. When multiple options are possible for some of the diagnostic systems (e.g., scintillation devices vs solid state detectors for tritium detection), tradeoffs were assessed. To accommodate the diagnostics previously mentioned, a new design for the window housing was developed. As the design of the original diamond window assembly underwent further development since the beginning of this project, an update of the general layout was required. The new layout presented here integrates the updates of the windows assembly with those to the diagnostics. To validate the concepts, a test bench was developed to carry out measurements under conditions similar to the operative ones.

A study on the effect of psychophysiological signal features on classification methods

Abstract The most important factor affecting the performance of a BCI (Brain Computer Interface) systems, is classification feature set. Choosing the right features to increase the success of classification is the key point. In BCI systems, signals from brain are used to store into dataset. In this study, BCI Competition III dataset 1 consisting of ECoG (Electrocorticography) signals is preferred. In the first part, in order to decrease the processing load, the number of channels are reduced by eliminating channels (electrodes) which have low separation success. We developed new algorithm ADA (Arc Detection Algorithm) based on visual channel selection to determine quickly optimal channel subset. Than obtained Wavelet coefficients by Discrete Wavelet Transform (DWT) and determined classification features from Wavelet coefficients. These features are used to classify by KNN (K Nearest Neighbors), SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis) by different feature set combination. The classification successes of feature combinations which are used in classification are compared. The impact on the classification performance of the right channel and the right property choice is observed. Test results are made with different frequency bands are compared with the same feature set. As a result, the highest classification accuracy of 95% was obtained by selected channels and feature.

A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

PurposeA multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection.Design/methodology/approachThe proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected.FindingsTypical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold.Practical implicationsThe arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development.Originality/valueThis proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

A neural network gravitational arc finder based on the Mediatrix filamentation method

Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements derived from the Mediatrix Filamentation Method as entries to an Artificial Neural Network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input to the ANN after a preselection. However, this completeness drops to $\sim$ 70% on the HST images. The false detections are of the order of 3% of the objects detected in these images. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

Detecting Ellipses in Embryo Images Using Arc Detection Method with Particle Swarm for Blastomere-Quality Measurement System

The objective of this paper is to present a novel method, based on a swarm intelligence algorithm, for ellipse detection in digital images of embryo. The process is carried out in several stages. First, edge detection is performed on the image. Then, line segments in the image are detected, and potential elliptical arc segments are extracted from the line segments. Afterward, the detection process is carried out using the Particle Swarm Optimization (PSO) method, which utilize the calculation of the fitness function from the arc segment previously detected. The PSO technique, which is the idea behind the proposed algorithm, is used to find the actual ellipses by combining potential elliptical arcs. The best combination of potential arcs is determined by means a voting technique that utilizes three important points on the arc, namely the starting point, midpoint, and endpoint, so the voting is more efficient than doing the voting for every single pixel in the image. Furthermore, this method is used an embryo image that has following the characteristics: multiple ellipses, a lot of noise, an incomplete ellipse, low image contrast, and overlapping cells. Experiment show that the proposed method detects the ellipses better than do several voting-based ellipse detection methods such as RHT, IRHT, and PSORHT. On the other hand, the experiments show that the proposed method has a higher average hit rate than do other methods. This research is used to increase the success rate of In-Vitro Fertilization (IVF).

Discovery of a fourth arc in Abell 2626 at 610 MHz with the GMRT: spectral properties and possibilities for the origin

Abstract We report the discovery of a fourth eastern arc (Arc E) towards the cool-core cluster Abell 2626 using 610 MHz Giant Metrewave Radio Telescope observations. Three arcs towards north, west and south were known from earlier works at 1400 MHz and proposed to have originated in precessing radio jets of the central active galactic nucleus. The 610–1400 MHz integrated spectral indices of the arcs are in the range 3.2–3.6 and the spectral index map shows uniform distribution along the lengths of the arcs. If associated with A2626, the arcs have linear extents in the range 79–152 kpc. The detection of Arc E favours the scenario in which a pair of bipolar precessing jets were active and halted to produce the arc system. Based on the morphological symmetry and spectral similarity, we indicate a possible role of gravitational lensing. Further, high-resolution low-frequency observations and measurements of the mass of the system are needed to disentangle the mystery of this source.

Mechanisms of electric arc detection based on current waveform spectrum and incremental decomposition analysis

Mechanisms of electric arc detection based on current waveform spectrum and incremental decomposition analysis

Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier.

EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM

A portable spectrometer based on a linear CCD is designed with real-time acquisition and processing of spectral data in the welding process of aluminum alloys. An innovative method is introduced to diagnose and detect porosity defects. The method extracts several characteristic spectral lines and calculates the intensity ratio between H I and Ar I. The intensity ratio is used to diagnose extraordinary cases of hydrogen content. Empirical mode decomposition (EMD) is used to acquire adaptive decomposition of the ratio signal, which has been proved to have better performance in eliminating the influence of pulse current on the ratio signal than wavelet packet transform. Experiments based on X-ray inspection are designed to verify the proposed method. Monitoring of the arc atmosphere and detection of porosity under different welding processes is achieved by extracting the feature parameters. An improved support vector machine (SVM) classification model based on a genetic algorithm (GA) is built in order to guarantee accurate estimation of different types of porosity defects.

A Study on the Arc Detection Algorithm via Analysis of the Arc's Electric Characteristics

A Study on the Arc Detection Algorithm via Analysis of the Arc's Electric Characteristics

Application of Optical Spectrophotometry for Analysis of Radiation Spectrum Emitted by Electric Arc in the Air

This paper presents the results of measurement and analysis of optical radiation emitted by a free burning electric arc. The aim was to determine the application possibilities of optical spectrophotometry for detection of electric arcs. The research works considered electric arc generated with a constant voltage supply between two copper electrodes in the air, carried out under laboratory conditions. A high resolution optical spectrophotometer was used for registration of optical radiation. The analyses involved determination of two dimensionless descriptors obtained for the gathered spectra. Moreover, for each of the registered intensity distributions, the energy values were calculated for three frequency ranges. Based on the measured signals, the possibility of application of spectrophotometry for the optical radiation analysis was confirmed. The analysis indicated that the most energy of optical radiation is detected for the range of 200–780 nm, while above 780 nm almost no optical energy is emitted. Spectrophotometric studies performed in the UV-NIR range are of interest since one can obtain information about the structural defects (at lower wavebands) or impurities and/or point defects (at low energies bands). It was also stated that the obtained descriptors may be applied for diagnosis and identification of electric arc purposes.