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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.

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.

RF arc detection is a key operational and safety issue for ICRF systems. Dedicated measurements on a RF test-stand were made in order to characterize the optical signature of RF arcs (time and spectrum) to assess the potential of optical arc detection on ICRF systems. Time-resolved intensity measurements were carried out using high-speed Si photodetectors with different bandpass filters. The rise time of the arc emission, the light intensity and the time evolution of the arc under different pressure conditions are discussed. Spectral signature of the arcs was obtained using spectrometers in the visible and UV range. It is shown that the arc emission spectrum is independent of the gas pressure and that it is mainly dominated by the stainless steel components of the test bench. The results of the RF arc optical characterization is discussed with a view on the implementation of an optical arc detection system for an ICRF antenna on a fusion machine, both for sections viewing the plasma light as for private vacuum sections. © 2011 Pierre Dumortier Published by Elsevier B.V. All rights reserved.

14th European Conference on Computer Vision (ECCV) -- OCT 08-16, 2016 -- -- Amsterdam, NETHERLANDS, WOS: 000389383900031, In this study, we propose an ellipse detection method which gives prospering results on occlusive cases. The method starts with detection of edge segments. Then we extract elliptical arcs by computing corners and fitting ellipse to the pixels between two consecutive corners. Once the elliptical arcs are extracted, we aim to test all possible arc subsets. However, this requires exponential complexity and runtime diverges as the number of arcs increases. To accelerate the process, arc pairing strategy is deployed by using conic properties of arcs. If any pair found to be non-coelliptic, then arc combinations including that pair are eliminated. Therefore the number of possible arcs subsets is reduced and computation time is improved. In the end, ellipse fitting is applied to remaining arc combinations to decide on final ellipses. Performance of the proposed algorithm is tested on real datasets, and better results have been obtained compare to state-of-the-art algorithms.

A guided radar arc detection and localization system has been designed, fabricated, installed in the feed line to one of the resonant loops on the 285/300 FW antenna, and successfully tested during vacuum conditioning. The system injects a train of binary phase-modulated pulses at a carrier frequency of 25 MHz up-shifted to around 450MHz into the main high power transmission line connected to the antenna through a septate coupler and a circulator. The pulses are reflected by arcs, and the time delay provides the distance to the arc. The reflected signals are analyzed in real time, with a time response sufficient to provide active arc detection as well as localization. RF pulses have been injected into the antenna at a power level of up to 650kW. The arc location was varied by either puffing gas into the vacuum vessel, in which case arcs always occurred in the antenna, or injecting RF without a gas puff, in which case the arcs almost always occurred in the transmission line feeding the antenna. The localization obtained during these initial tests had a relatively low resolution of about 2 m, but arcs occurring inside or outside the antenna could clearly be differentiated and corresponded with the expected location. The septate coupler proved fully compatible with the antenna feed and matching network and improved performance significantly in comparison to the use of directional couplers.

The GUIDAR technology has been proposed for the detection of electric arcs in the transmission lines for antennas for plasma heating and current drive. After a preliminary study to assess the feasibility of this technique, some experimental tests with real arcs were conducted on the MXP testbed installed at IPP, Garching. The low frequency (25MHz) GUIDAR signal, made of a sequence of short phase‐modulated impulses, is up‐shifted to around 400MHz and injected into the transmission line by mean of a directional coupler. The echoes are then extracted with another directional coupler and down‐shifted again for the processing. The analysis is performed at a pulse repetition frequency of 120–165kHz, enabling an arc detection within 6–8μs. Tests have shown encouraging results to demonstrate the capability of the GUIDAR system to easily detect both high voltage and, most important, low voltage arcs. The possibility of locating the arcs has also been addressed in the testbed with simulated arcs. The insensitivity of the method to slow changes of the line voltage standing wave ratio (mimicking antenna load variations) was also tested.

RF arc detection is a key operational and safety issue for ICRF systems. Dedicated measurements on a RF test-stand were made in order to characterize the optical signature of RF arcs (time and spectrum) to assess the potential of optical arc detection on ICRF systems. Time-resolved intensity measurements were carried out using high-speed Si photodetectors with different bandpass filters. The rise time of the arc emission, the light intensity and the time evolution of the arc under different pressure conditions are discussed. Spectral signature of the arcs was obtained using spectrometers in the visible and UV range. It is shown that the arc emission spectrum is independent of the gas pressure and that it is mainly dominated by the stainless steel components of the test bench. The results of the RF arc optical characterization is discussed with a view on the implementation of an optical arc detection system for an ICRF antenna on a fusion machine, both for sections viewing the plasma light as for private vacuum sections. © 2011 Pierre Dumortier Published by Elsevier B.V. All rights reserved.

The original publication is available at www.springerlink.com; International audience; In this paper we present a method to robustly detect circular arcs in a line drawing image. The method is fast, robust and very reliable, and is capable of assessing the quality of its detection. It is based on Random Sample Consensus minimization, and uses techniques that are inspired from object tracking in image sequences. It is based on simple initial guesses, either based on connected line segments, or on elementary mainstream arc detection algorithms. Our method consists of gradually deforming these circular arc candidates as to precisely fit onto the image strokes, or to reject them if the fitting is not possible, this virtually eliminates spurious detections on the one hand, and avoiding non-detections on the other hand.