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With the rapid growth of China’s high-speed railway mileage, relevant safety technologies of railway operation are very urgent at present. One of the crucial parts is the pantograph–catenary system, which is directly related to the power supply safety of trains in high-speed railways. To ensure the safe operation of the pantograph–catenary system and eliminate the hidden danger during the power supply of trains, from 2011 in China, an automatic detection and monitoring system (called 6C system) of pantograph–catenary in high-speed railways was gradually established to timely discover and report all the potential defects and evaluate the performance of the pantograph–catenary system. The 6C system can measure the dynamic and static geometry parameters of the catenary and detect the defective working states of components and the operation conditions of the pantograph–catenary system. Considering the feedbacks from railway administrations, the 6C system can also realize a fine-grained detection and diagnosis of the pantograph–catenary and significantly improve the detection efficiency. In this article, the whole architecture of the 6C system is presented in detail, including six relatively independent parts for the detection and monitoring of pantograph–catenary systems and a data processing center with a three-level structure. Especially, some critical technologies for automatic detection and monitoring of pantograph–catenary systems are introduced. In the end, some new techniques of detection and monitoring are discussed, and suggestions are made for the future development of detection and monitoring systems of pantograph–catenary in high-speed railways. [ABSTRACT FROM AUTHOR]

Fault frequency of catenary is related to meteorological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary operation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified correctly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided. [ABSTRACT FROM AUTHOR]

Auto‐transformer (AT) power traction networks with a parallel connection are the unique mode on high‐speed electric railways in China. The up line and down line of power traction networks are connected at substation (SS), AT post (ATP) and sectioning post (SP) through busbars, circuit breakers and disconnectors. Currently, when a fault occurs on power traction networks, the feeder line protection devices at the up line and down line in SS trip simultaneously. After that, the nonfault line and instantaneous fault line recover by means of autoreclosing. This tripping mode enlarges the power supply‐interrupted region. Based on the analysis of circuit networks of power traction networks on high‐speed electric railways, the interrelations of currents in SS and ATP and in SS and SP are derived. When a fault occurs on one side of the power traction networks, the feeder currents in ATP and SP flow from the nonfault side to the fault side. The relation between feeder currents in SS and ATP and between feeder currents in SS and SP reflect this characteristic. A criterion for discriminating the fault side and selective‐tripping scheme of breakers is proposed based on the correlation analysis of feeder current fault components in multisite. The simulation results show that this scheme can discriminate fault side and make a selective trip rapidly and correctly. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

Frequency response analysis (FRA) is an effective approach for detecting mechanical damage in the windings of a transformer. However, the application of FRA to estimate the winding condition of autotransformers (ATs) employed in the China High-Speed Railway is problematic. Because the split windings of the ATs are connected inside the tank, it is impossible to measure the FRA curves for each split winding outside the tank directly. Therefore, it is difficult to identify fault winding and effectively diagnose the overall condition of the windings. This paper explores the features of FRA curves under different fault conditions to help distinguish the fault winding. An equivalent circuit model is built for the AT with split windings, and the electrical parameters of the windings are determined using the finite-element method. The model is then employed to simulate typical axial displacement faults. The results show that the first antiresonance and resonance of the frequency response for connections involving series windings or series and common windings may be visible features for diagnosing winding faults and the fault level. Specifically, the first and second resonances of the frequency response for the common winding connection may be regarded as the primary feature for identifying fault windings. [ABSTRACT FROM PUBLISHER]

High-frequency harmonic resonance has occurred frequently in the traction power-supply system (TPSS) when high-speed trains that are equipped with pulsewidth-modulation converters were serviced in the China High-Speed Railway (HSR). This phenomenon can result in serious voltage distortion, overheating of electrical components, and electromagnetic interference (EMI) of communication circuits. In order to address this issue, a complete mathematical model of the 2\,\times\,27.5-kV ac railway system, by means of the nodal admittance matrix, is presented to attain equal modeling of TPSS and modern train for harmonic studies in this paper. The impact of the train on resonance behavior of TPSS by employing resonance-mode assessment and modal sensitivity analysis are investigated. Finally, numerical simulation results using the presented model are compared to measurements results of the China HSR to verify the accuracy of the TPSS model and Norton model of the train, and proved that the method of resonance frequency shifts described is doable. The comparison results illustrate that the proposed model and harmonic resonance assessment can be useful for determining and mitigating the harmonic resonance problems in TPSS as well as the power system. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is required for numerous developmental processes, such as flowering and reproduction, in maize (Zea mays L.). It is also an essential nutrient in fertilizers to sustain high yields in farmlands with insufficient available phosphate, particularly in acid soils in China. Flowering time is a key factor influencing the adaptation of maize to the local environment and is associated with high yields. However, few studies have focused on the genetic characteristics of flowering time under low phosphate conditions. In this study, a recombinant inbred line (RIL) population derived from a cross between X178 and 9782 was evaluated for three flowering time traits under two phosphates (Pi) levels at four locations. We found that low phosphate significantly delayed the flowering stage of maize and proposed the hypothesis of "Phosphorus Accumulation Effect". Mixed-model-based composite interval mapping was used to dissect the genetic basis of the flowering time traits. A total of 41 quantitative trait loci (QTL) was identified for the three flowering time traits through four procedures. Among these 41 QTL, 38 focused on four hot regions, including bin 2.02, bin 2.03, bin 5.04, and bin 6.01. These regions and linked markers should be the targets for fine mapping, map-based cloning, and marker-assisted selection of flowering time and phosphate utilization efficiency. [ABSTRACT FROM AUTHOR]

A variety of Chinese textual operational text data has been recorded during the operation and maintenance of the high-speed railway catenary system. Such defect text records can facilitate defect detection and defect severity analysis if mined efficiently and accurately. Therefore, in this context, this paper focuses on a specific problem in defect text mining, which is to efficiently extract defect-relevant information from catenary defect text records and automatically identify catenary defect severity. The specific task is transformed into a machine learning problem for defect text classification. First, we summarize the characteristics of catenary defect texts and construct a text dataset. Second, we use BERT to learn defect texts and generate word embedding vectors with contextual features, fed into the classification model. Third, we developed a deep text categorization network (DTCN) to distinguish the catenary defect level, considering the contextualized semantic features. Finally, the effectiveness of our proposed method (BERT-DTCN) is validated using a catenary defect textual dataset collected from 2016 to 2018 in the China Railway Administration in Chengdu, Lanzhou, and Hengshui. Moreover, BERT-DTCN outperforms several competitive methods in terms of accuracy, precision, recall, and F1-score value.