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Arc detection is crucial for ensuring the safe operation of power systems, where timely and accurate detection of arcs can prevent potential hazards such as fires, equipment damage, or system failures. Traditional arc detection methods, while functional, often suffer from low detection accuracy and high computational complexity, especially in complex operational environments. This limitation is particularly problematic in real-time monitoring and the efficient operation of power systems. In order to solve these problems, this paper proposes a method of arc detection based on deep learning, called arc multi-scene detection (ArcMSD), which leverages deep learning techniques to address these challenges. The primary distinction of this method lies in its enhancement of the Inception V3 model. This paper has redesigned the original Inception module by incorporating a guided anchor mechanism, an attention mechanism, and upsampling techniques to optimize detection performance. The improved Inception V3 network uses an attention mechanism to allow the model to focus on arc features in complex backgrounds, which can also prevent the model from overfitting. It performs upsampling and fusion with low-level features in the model. The fused features have better arc discrimination capabilities than the original input features, which better improves the accuracy of the model. In order to adapt to arcs with large size differences and improve detection efficiency, the guided anchor is selected to adjust the anchor generation algorithm. In terms of dataset, continuous frame images are intercepted from the video of Integrated Supervision and Control System (ISCS), and image preprocessing operations are performed to improve the model's detection accuracy of pantograph arcs. Experimental results show that the mean Average Precision (mAP) of the deep learning model proposed in this article is 95.4%, which is far better than other models, thus verify the method's efficacy. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic systems have encountered unprecedented opportunities for development given their environmentally friendly nature and flexible power generation characteristics. However, numerous connecting lines and taps within the distributed photovoltaic system can be subject to insulation issues, which will consequently cause direct current (DC) arc faults and severe electrical fire hazards. Moreover, the power semiconductor devices in the photovoltaic inverter can introduce common-mode noises to the DC current, resulting in unwanted tripping of the DC arc fault detector. The study proposes an arc fault detection method utilizing a deep residual shrinkage network (DRSN) to address this issue, thereby precisely detecting DC arc faults. A test platform for series arc faults in photovoltaic systems is built. The arc current data are collected for characteristic analysis in time and frequency domains to determine which bandwidth is preferred for the algorithm. The model's depth is increased by introducing residual connections, enhancing its feature extraction, and improving noise reduction capabilities. The residual shrinkage network has been enhanced to prevent a computation increase from increased network depth. Introducing a convolutional auto-encoder for data dimension reduction has decreased neural network parameters, thereby improving training speed. A prototype for detecting photovoltaic DC arc faults was constructed using Raspberry Pi 4B, validating the practical application value of the proposed method. Experimental results demonstrate that the prototype for detecting photovoltaic DC arc faults successfully fulfills the real-time detection standard of the conduction test. [ABSTRACT FROM AUTHOR]

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan. [ABSTRACT FROM AUTHOR]

PV arc-faults can cause fires, damage property, and endanger people's lives. This paper proposes a method for detecting DC arcs using artificial intelligence (AI). The four steps for arc detection are thoroughly described. After removing the low-frequency range (41 kHz) and high-frequency range (>102.5 kHz) components, the middle frequency range is left for arc analysis. For AI analysis, eight inputs are used. The time sequence for the tasks is also explained, where the parallel task configuration is adopted to save the time. Furthermore, AI model training for arc detection is described, including both offline and online training. In addition, three different types of arc detection system architectures are depicted. There are three layers in the architectures: the PV-end layer, the inverter-level layer, and the cloud layer. Depending on the architecture, the algorithm is located in different layers. Furthermore, the hardware of the arc detection system is explained, as is the self-testing circuit. Finally, an experimental platform is built, and experimental results are presented to validate the proposed method.

This paper is aimed at making new proposals for developing future Electro-Magnetic Compatibility (EMC) standards tailored to DC microgrids in a frequency range between 9 and 500 kHz. In particular, new EMC proposals are made to reduce Electro-Magnetic Interference (EMI) with arc hazard detection and narrowband power line communication (PLC). To achieve this, first, arc detection requirements, PLC standards and existing EMC standards are reviewed. Next, new proposals are made to specify EMC requirements for equipment in DC microgrids in terms of conducted emission, immunity (9–500 kHz) and minimum impedance requirement (>40 kHz). The minimum impedance requirement is a new type of requirement and the relevant compliance testing method is developed. The new EMC proposals also distribute frequency bands to support arc detection and narrowband PLC. Then, to show the feasibility and advantage of proposed EMC codes, this paper develops a new arc detection method, which relies on only measuring the arc noise voltage (40–100 kHz) in a single point of the grid and does not need one or more current measurements. A total of three test cases are presented to show the feasibility of the arc detection method and the significance of having an EMC minimum impedance requirement. The executed tests for this paper also show that new EMC proposals are feasible and promising for DC microgrids. This concept and approach are the major novelties of this paper. The specific EMC threshold levels for conducted noise, immunity, and impedance within a frequency range between 9 and 500 kHz will need to be further fine-tuned based on the microgrid application parameters and further gathering of experimental data. [ABSTRACT FROM AUTHOR]

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.

Various studies on arc detection methods are described. Series AC arc is detected based on the characteristics extracted from arc voltage, frequency, and time domain of the current. Methods of arc detection using artificial intelligence have been studied previously. In the present study, the performance of multiple methods is analyzed by comparing different input parameters and artificial neural networks. In addition to the input parameters presented in the literature, the performance is compared and analyzed using the following parameters: zero-crossing period, frequency average, instantaneous frequency, entropy, combination of fast Fourier transform (FFT) and maximum slip difference, and combination of FFT and frequency average. These parameters and different neural networks are studied in the bounded and unbounded case, and the performance is compared. For different combinations of neural networks and input parameters, another research question is to identify the input parameters to be used if the number of training data is limited. Moreover, this study investigates the change in detection rate depending on the number of training samples. As a result, the minimum dataset size required to obtain the final detection rate is identified. [ABSTRACT FROM AUTHOR]

The increasing household loads make series arc faults more complex, which are difficult to be detected by traditional circuit breakers and lead to the frequent occurrence of residential fire accidents. In this paper, a comprehensive approach of complex load recognition and series arc detection is proposed on the basis of principal component analysis and support vector machine (PCA-SVM) combination model. Several typical loads were selected and analyzed, especially nonlinear and complex loads like power electronics load and multi-state load. Three time-domain parameters, maximum slip difference (MSD), zero current period (ZCP) and maximum Euclidean distance (MED), and nine frequency-domain harmonics information are collected to complex waveforms. To decrease the computation cost and further to enhance the response velocity, all the time-domain and frequency-domain information were blended and dimensionally reduced to three parameters by principal component analysis (PCA). Prior to the series arc detection, load recognition is trained and completed with the artificial intelligence (AI) algorithm. At last, the comprehensive model of load recognition and series arc detection is achieved based on a support vector machine (SVM). The accuracy of load recognition and series arc detection reaches 99.1% and 99.3%, respectively, demonstrating the excellent performances of the intelligent approach to diagnose the series arcing activities in modern household applications.

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. [ABSTRACT FROM AUTHOR]

Electric arcing due to contact interruption between the pantograph and the overhead contact line in electrified railway networks is an important and unwanted phenomenon. Arcing events are short-term power quality disturbances that produce significant electromagnetic disturbances both conducted and radiated as well as increased degradation on contact wire and contact strip of the pantograph. Early-stage detection can prevent further deterioration of the current collection quality, reduce excessive wear in the pantograph-catenary system, and mitigate failure of the pantograph contact strip. This paper presents a novel arc detection method for DC railway networks. The method quantifies the rate-of-change of the instantaneous phase of the oscillating pantograph current signal during an arc occurrence through the Hilbert transform. Application of the method to practical pantograph current data measurements, demonstrates that phase derivative is a useful parameter for detecting and localizing significant power quality disturbances due to electric arcs during both coasting and regenerative braking phases of a running locomotive. The detected number of arcs may be used to calculate the distribution of the arcs per kilometre as an alternative estimation of the current collection quality index and consequently used to assess the pantograph-catenary system performance. The detected arc number may also contribute to lowering predictive maintenance costs of pantograph-catenary inspections works as these can be performed only at determined sections of the line extracted by using arcing time locations and speed profiles of the locomotive.

In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified.

pantograph-catenary system is one of the critical components used in electrical trains. It ensures the transmission of the electrical energy to the train taken from the substation that is required for electrical trains. The condition monitoring and early diagnosis for pantograph-catenary systems are very important in terms of rail transport disruption. In this study, a new method is proposed for arc detection in the pantograph-catenary system based signal processing and S-transform. Arc detection and condition monitoring were achieved by using current signals received from a real pantograph-catenary system. Firstly, model based current data for pantograph-catenary system is obtained from Mayr arc model. The method with S-transform is developed by using this current data. Noises on the current signal are eliminated by applying a low pass filter to the current signal. The peak values of the noiseless signals are determined by taking absolute values of these signals in a certain frequency range. After the data of the peak points has been normalized, a new signal will be obtained by combining these points via a linear interpolation method. The frequency-time analysis was realized by applying S-transform on the signal obtained from peak values. Feature extraction that obtained by S-matrix was used in the fuzzy system. The current signal is detected the contdition as healthy or faulty by using the outputs of the fuzzy system. Furthermore the real-time processing of the proposed method is examined by applying to the current signal received from a locomotive. [ABSTRACT FROM AUTHOR]

We propose a novel line-detection algorithm that avoids interpreting a smooth curve in an image as many straight segments. The algorithm starts from a contiguous (connected) set of edge chains extracted by the Edge Drawing Parameter Free (EDPF) algorithm such that a problem of line detection in the image space is transformed to a problem of line segments and circular arcs identified for each edge chain. The core of our approach is the proposed orientation transformation (OT) algorithm based on principal component analysis; hence, our algorithm is named OTLines. With the OT algorithm, each edge chain is not only decomposed into a series of straight line segments and smooth curve candidates at the estimated corners but is also mapped to a series of oblique lines and horizontal lines in a new space called the orientation space. Based on the results of the transformation, line segments and circular arcs can be detected simultaneously in both the orientation and image spaces using principal component analysis. The performance of our proposed algorithm is validated using a set of typical images. We also compare our algorithm with existing algorithms. Our novel algorithm is very effective at segmenting the lines in synthetic and natural images. No parameters in the proposed algorithm must be tuned during its application. [ABSTRACT FROM AUTHOR]

A measured contact-loss ratio is used to estimate current-collection performance. There are two types of methods used to estimate the contact-loss ratio. One is to measure all the contact losses, including small ones; the other is to measure only contact losses that are greater than 5 ms in duration. The extent of wear of a contact strip caused by arc discharges varies as a function of the amount of electric charge. This fact makes it possible to manage the wear on a contact strip. This paper shows a calculation method for the amount of electric charge based on the output of a contact-loss measurement as well as the expected lifetime of the contact strip and the creation of a maintenance threshold level for of the contact strip. [ABSTRACT FROM AUTHOR]

The maintenance of the pantograph–catenary system is a crucial problem in railway inspection systems. The quality of current collection depends on maintaining good contact between a moving pantograph and an overhead contact wire. Problems in the current collection system lead to the damage and disruption of railway traffic. Pantograph arcing occurs between the pantograph and the overhead wire because of the wear of the contact strip. This paper proposes an automatic inspection system for detecting the pantograph and arcs that occur using a firefly optimization algorithm. The proposed method is able to localize the pantograph as a rectangular region and detect the burst of arcing under different illumination and weather conditions simultaneously. The firefly algorithm based Otsu method is used to detect arcs that occur in this region. The proposed approach can serve as an automatic monitoring system and can detect the occurrence of arcing due to loss of contact. The proposed method was applied to four real pantograph videos and efficient results were obtained. [ABSTRACT FROM AUTHOR]

We present in this paper an algebraic derivative method of the line current in order to detect the presence of series arcs in an AC or DC electrical installation. The first derivative is computed from a limited Taylor-McLaurin series transposed in Laplace space. The temporal estimation is achieved by integration over a sliding window of the product of a particular polynomial with the instantaneous current. The discrete version can be synthesized by a simple FIR filter. The tests, with and without series arc, are conducted on experimental currents (3–12 A) measured on domestic loads (resistors, vacuum drill, dimmer). The sampling frequency is set to 1 MHz. Short integration times (50 μs in AC and 200 μs in DC) are sufficient to observe, with high contrast, the derivative peaks due to the arc ignition. The detection is then performed by comparing the derivation filter output to its instantaneous noise level. The response time is equal to the integration duration. This method, simple to set up and easy to implement, is ideally suited for installations that do not use load switching current. [ABSTRACT FROM AUTHOR]

This paper provides a technological review of arc-flash protection of air insulated switchgear. It covers the whole range starting from switchgear design aspects until ultra-fast arc elimination. Special attention is paid to proactive technologies enabling preemptive detection of slowly developing faults. Various arc faults indicating phenomena are examined, and several sensor technologies for online monitoring are evaluated. Because preventive or predictive measures cannot totally eliminate the risk of arc faults, reactive protection by fast operating protection is justified. Two major reactive protection approaches are discussed: the current-limiting approach and the arcing time based approach. The benefits of protection based on simultaneous detection of light and overcurrent are explained. Finally the paper discusses arc elimination technologies and evaluates the concerns related to short-circuit devices. [ABSTRACT FROM AUTHOR]

Predictive Maintenance, Prognostics and Reliability Centered Maintenance approach are becoming more and more important in the railway sector to reduce costs of operation and to increase reliability and safety. In fact, these are fundamental to optimize the maintenance process, to define new measures and algorithms which locate faults, to monitor health conditions of subsystems and to estimate residual life of components. In some cases it's possible to use existing sensors and existing processing hardware to extract new information from the existing available data. It's clear that this is usually the best option because the benefit can be achieved with little or no cost at all. This paper describes the result of a study performed with the aim of detecting arcing events without the need of additional equipment mounted on board the train. A set of data relative to voltage and current collected on high speed trains along with a set of measurements coming from photosensors are available. The data are processed by the use of an advanced classification technique, Support Vector Machines, with the aim of extracting important information such as the time coordinate related to anomalies in the overhead contact line and the status of the pantograph contact strip. [ABSTRACT FROM AUTHOR]

• A special case study of ineffective protection with arc fault detection Device. • AFDD can be ineffective when detecting sparking are in the circuit with a switched-mode power supply. • AFDDs have been tested according to the IEC 62 606 standard. • Effectiveness of AFDD circuit breakers can be different in circuits with a resistance load. 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. [ABSTRACT FROM AUTHOR]

Bi-polar arcs require a high voltage difference between two closely spaced points. As an example, if there is excessive deposition or contamination on the deposition and or cover ring in a physical vapor deposition tool (PVD) tool, a DC bipolar arc can occur leading to ablation of underlying materials, wafer breakage, or chamber damage caused by the discharge. In some cases these incidents are not identified until numerous wafers have been processed. Therefore, it is essential to identify arcing at the time of the event. In this paper, we address arc detection in a PVD chamber. The electrostatic chuck (ESC) critical parameter(s) are captured with 1000 Hz sampling frequency and signal processing techniques such as FFT and wavelet transforms are used to improve the signal-to-noise ratio enhancing the ability to isolate arcs from raw data. This methodology can be implemented on other plasma chamber types. [ABSTRACT FROM AUTHOR]

A novel algorithm to detect circular arcs from a digital image is proposed. The algorithm is based on discrete curvature estimated for the constituent points of digital curve segments, followed by a fast geometric analysis. The curvature information is used in the initial stage to find the potentially circular segments. In the final stage, the circular arcs are merged and maximized in length using the radius and center information of the potentially circular segments. Triplets of longer segments are given higher priorities; doublets and singleton arcs are processed at the end. Detailed experimental results on benchmark datasets demonstrate its efficiency and robustness. [ABSTRACT FROM AUTHOR]

Abstract: Arc detection systems are developed for ICRH on ITER to prevent arcs from damaging the ICRF components. One of the detectors, the Sub-Harmonic Arc Detector (SHAD) is based on the detection of the frequencies emitted in the MHz range by arcs , and is consequently resilient to fast load changes. A prototype has been in operation on ASDEX-Upgrade for ten years and has demonstrated its capability to detect arcs. However, to reach the safety level required by ITER, the design has to be modified to eliminate the spurious detections triggered by plasma emission in the MHz range and to enhance the sensitivity to low-voltage arcs. These improvements, which must preserve the simplicity of the system to guarantee its reliability, are based on the experience of RF arcs in resonant structures acquired on ASDEX-Upgrade and on dedicated test-benches. We present the main features of arcs in time and frequency domains and their impact on the behaviour of the SHAD system. Several technological solutions are based on these data and have to support the harsh environment of a burning plasma experiment. The shielding, the location of the detector, the reliability can be affected by the nuclear radiations. These elements are integrated in the design to make of the SHAD a primary safety system for ICRF heating. [Copyright &y& Elsevier]

Abstract: Ion Cyclotron Radio Frequency (ICRF) antennas operating under high voltage to couple high power to fusion plasmas are at risk of electrical arcing. The standard Voltage Standing Wave Ratio (VSWR) system does not protect low impedance areas, which are used in antennas to achieve load tolerance to variations in plasma loading during Edge Localised Modes (ELMs). The Scattering Matrix Arc Detection System (SMAD) was designed to create additional protection for these areas, and 4 complete systems were implemented and tested on the ITER-like antenna (ILA) on JET. This paper describes the performance under relevant experimental conditions of load tolerance and/or high voltage and electrical arcing causing generator trip events. [Copyright &y& Elsevier]

Abstract: A simple and sensitive technique, which utilizes the sensing of both the discharge current and the visible light emission from the discharge, has been developed to detect the arc formation during the operation of a high repetition rate TEA CO2 laser. It is shown that the optical detection in the visible spectral region is more sensitive than that it is in the UV region and hence can be used more effectively to monitor the onset of arc formation, while the current signal is useful in the strong arc formation regime. We demonstrate that the trigger signals generated by both the methods can be employed for reliable protection of the laser system from catastrophic damage due to deleterious arc formation. [ABSTRACT FROM AUTHOR]

Abstract: A voltage breakdown in an ion cyclotron resonance heating (ICRH) system is usually detected as an abrupt change in the module of the reflection coefficient |Γ in| at the power source output. The same effect can be due to a large load variation. This paper presents the study of some new concept for detecting voltage breakdowns in ICRH Resonant Double Loop (RDL) systems, to be used in conjunction with the one identifying an arc by the detection of an excessive standing wave ratio (SWR), either at the operating frequency or within the band below the operating frequency. The method should be able to help discriminating fast load variations from voltage breakdowns in any of the in-vessel components under the torus vacuum, and therefore selecting the proper corrective action. [Copyright &y& Elsevier]

Abstract: Too high voltages can cause arcs to develop within coaxial transmission lines of ICRF antennas. To avoid damage, arcing must be detected and the RF power switched off. To ensure a high level of safety, the specifications of the detectors have to be based on a fine characterization of arcs. A test bench comprising a coaxial resonator and a fast acquisition system makes it possible to analyze the effects of breakdowns on power, voltage, pressure and DC current in the electrodes. For different types of breakdowns (multipactor, vacuum arc and gas discharge) and different configurations (presence or not of magnetic field, level of injected power), we investigate the processes involved in the specific development of an arc inside a RF coaxial line. The resulting classification of phenomena and the impact on the operational system requirements are discussed. [Copyright &y& Elsevier]

Abstract: Since 1999 and with the help of Institut fur Plasmaphysik (IPP) Garching, we have tested in the Tore Supra (TS) Ion Cyclotron Resonance Heating (ICRH) transmitter, arc security systems based on harmonics detection in a frequency band lower than the generator frequency. These systems have some advantages in comparison with traditional protection systems, which are based on use of a level on the V r/V f (reflected to forward voltage ratio) calculation. After some checking for adjustment, they do not use calculation, calibration with frequency or level. They can use RF signals from directional couplers or probes, so they are fully independent of the measurement systems. The detection bandwidth is independent of the antennae working frequencies. In TS the detection principle is insensitive to the cross talk between the antennae and to the antennae coupling variations. In 2001, we have built three systems based on our prototype in order to improve the protection of the three ICRH antennae Q1, Q2 and Q5. The RF principle used on TS ICRH transmitter is described. The results in the last 4 years on an ICRH transmitter module feeding a TS antenna are summarized and some examples typical signals are given. [Copyright &y& Elsevier]

Abstract: A method based on oriented connectivity that can automatically segment arc-like structures (solar loops) from intensity images of the Sun''s corona is introduced. The method is a constructive approach that uses model-guided processing to enable extraction of credible loop structures. Since the solar loops are vestiges of the solar magnetic field, the model-guided processing exploits external estimates of this field''s local orientations that are derived from a physical magnetic field model. Empirical studies of the method''s effectiveness are also presented. The oriented connectivity-based method is the first automatic method for the segmentation of solar loops. [Copyright &y& Elsevier]

Spread spectrum time domain reflectometry (SSTDR) and sequence time domain reflectometry have been demonstrated to be effective technologies for locating intermittent faults on aircraft wires carrying typical signals in flight. This paper examines the parameters that control the accuracy, latency, and signal to noise ratio for these methods. Both test methods are shown to be effective for wires carrying ACpower signals, and SSTDR is shown to be particularly effective at testing wires carrying digital signals such as Mil-Std 1553 data. Results are demonstrated for both controlled and uncontrolled impedance cables. The low test signal levels and high noise immunity of these test methods make them well suited to test for intermittent wiring failures such as open circuits, short circuits, and arcs on cables in aircraft in flight. [ABSTRACT FROM PUBLISHER]

Electric arcing due to contact interruption between the pantograph and the overhead contact line in electrified railway networks is an important and unwanted phenomenon. Arcing events are short-term power quality disturbances that produce significant electromagnetic disturbances both conducted and radiated as well as increased degradation on contact wire and contact strip of the pantograph. Early-stage detection can prevent further deterioration of the current collection quality, reduce excessive wear in the pantograph-catenary system, and mitigate failure of the pantograph contact strip. This paper presents a novel arc detection method for DC railway networks. The method quantifies the rate-of-change of the instantaneous phase of the oscillating pantograph current signal during an arc occurrence through the Hilbert transform. Application of the method to practical pantograph current data measurements, demonstrates that phase derivative is a useful parameter for detecting and localizing significant power quality disturbances due to electric arcs during both coasting and regenerative braking phases of a running locomotive. The detected number of arcs may be used to calculate the distribution of the arcs per kilometre as an alternative estimation of the current collection quality index and consequently used to assess the pantograph-catenary system performance. The detected arc number may also contribute to lowering predictive maintenance costs of pantograph-catenary inspections works as these can be performed only at determined sections of the line extracted by using arcing time locations and speed profiles of the locomotive. [ABSTRACT FROM AUTHOR]

In this paper, a series arc was simulated under resistive load and motor load, which are mainly used in small ships, and the arc signal was analyzed using discrete wavelet transform. After calculating the correlation coefficient between the single arc pulse and the wavelet, Biorthogonal (bior) 3.1 was selected as the optimal mother wavelet, and the signal was analyzed using multiresolution analysis. From the results, arc signals were distributed in the detail components D2, D3, D4 and D5, corresponding to a frequency range of 19.5–312.5 kHz, with the optimal arc signal extracted based on these values. In addition, in order to distinguish between arc and normal conditions, signal energy was analyzed. By applying the magnitude and signal energy analysis method, the DC series arc generated in the power distribution system of a shipboard was identified. [ABSTRACT FROM AUTHOR]

• The JET ILA S-Matrix Arc Detection (SMAD) protection system has been improved and fully re-commissioned. • The SMAD error remains small in the operating frequency range (29–51 MHz) and under all different plasma conditions tested. • The time delay to issue a trip is typically 6 – 8 μs. • The delay between the trip and the removal of the power on the lines is measured to be about 25 μs. • The system is reliable, robust and within the JET specifications. Arc detection is an essential protection system for high power RF systems. It is commonly realised by monitoring the Voltage Standing Wave Ratio (VSWR) in the transmission lines. The JET ILA is a load-tolerant ICRF antenna with a low impedance section, for which the standard VSWR protection is ineffective. The Scattering-Matrix Arc Detection (SMAD) was proposed and implemented on JET to protect the low-impedance section around the T-junction against arcing. It is based on a consistency check of the RF signals around this section using a table of coefficients obtained from RF modelling. The paper reviews the SMAD principle, its commissioning on JET and the latest improvements to the system. It shows an example of operation and the time response of the system is analysed. The latest improvements have increased the robustness and reliability of SMAD, making it a choice of arc detection system for critical sections on existing and future RF systems. [ABSTRACT FROM AUTHOR]