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The catenary support components (CSCs) are the most important devices in high-speed railways to support contact lines for powering trains. To estimate the states of CSCs, it is very necessary to locate their positions in a monitoring system based on computer vision. Considering the application scenarios and characteristics of CSCs, an automatic and quick positioning system is designed in this paper to simultaneously position the multiscale CSCs with 12 categories. In the system, an effective framework called CSCs network (CSCNET) is presented, which cascades the coarse positioning network and the fine positioning network to reduce multiscale differences between different CSCs. In the coarse positioning network, a new unsupervised clustering algorithm based on the relative positioning information is proposed to classify the catenary images. Then, a convolutional neural network (CNN) classification network is trained to extract the structural features of catenary images and generate the proposal regions with labels. In the fine positioning network, a modified CNN positioning framework is applied to obtain the accurate positions of CSCs based on the coarse positioning results. Due to the special lightweight structure with a classification network, the relative position information is applied and makes the CSCNET sensitive to small-scale components. The experimental results from some high-speed railway lines in China show that the proposed system has obvious advantages in the CSCs positioning. The mean average precision and frames per second of CSCNET reach 0.837 and 2.17, respectively. Compared with some popular convolutional networks [faster region-based CNN (Faster R-CNN), etc.] and a typical positioning method, the proposed system significantly improves the AP without increasing the computational time. [ABSTRACT FROM AUTHOR]

The dynamic behavior of phasor measurement units (PMUs) is not only the key point to dynamic phasor algorithm improvement but also the basis for the successful implementation of PMUs in power system dynamic security monitoring and control. In this paper, the dynamic performance of PMUs is evaluated, and their dynamic behaviors are analyzed. PMUs from the four largest Chinese manufacturers are tested through the established PMU test system, and their dynamic performance is comprehensively evaluated. The dynamic behaviors of the PMUs are also analyzed. Analysis shows that the spectral leakage and the averaging effect of the discrete Fourier transform (DFT) under dynamic conditions are the key reasons that lead to their dynamic behaviors. The theoretical results are consistent with the experimental data and the simulation. Finally, to eliminate the spectral leakage, the DFT with a digital low-pass filter is adopted to obtain more precise phasor measurements. [ABSTRACT FROM PUBLISHER]

A new harmonic power standard has been developed at the National Institute of Metrology (NIM), Beijing, China, for the calibration of harmonic power analyzers under nonsinusoidal conditions at fundamental frequencies of 50 and 60 Hz. The standard is based on digital sampling techniques that do not require synchronization. A compensation algorithm is presented in this paper. A new definition of the uncertainty for harmonic measurement is proposed that is referred to the fundamental. A characterization signal and its application are introduced. Results have shown that over its operating range of up to 50 A, 500 V, and the sixtieth-order harmonic, the harmonic power standard has uncertainties (k = 2) of less than 30 μV/V, 36 μA/A, and 42 μW/VA for voltage, current, and power measurements, respectively. [ABSTRACT FROM AUTHOR]

The objective of this paper is to find a robust and sustainable positioning approach in Hong Kong, where the Global Positioning System (GPS) signal is frequently blocked by high buildings, traffic tunnels, etc. After evaluating several kinds of complementary positioning means, the GPS/dead-reckoning (DR)/map-matching (MM) integrated positioning technology is selected. During navigation, if the GPS signal is good enough, the GPS/DR/MM mode will be used; otherwise, the DR/MM mode will be used, and the MM algorithm will correct the DR system in a timely fashion. Generally, if the vehicle is running in a straight road, heading-related parameters in the DR system will be corrected, and if the vehicle makes a turn, both distance- and heading-related parameters will be corrected. Unfortunately, existing MM algorithms show poor performance near a road crossing or in a dense road network. To resolve this problem, based on road traverses and a linear heading-change model, an MM algorithm that considers historical information is proposed. First, a unified point-based MM calculating framework is presented, and the disadvantages of several kinds of existing MM algorithms are also given. After that, a single-line-single-direction road network model is presented, and the MM algorithm is always executed on road traverses instead of a single road so that historical information can be easily kept. By adopting the linear heading-change model, the problem of no analytical solution in the MM that considers heading information is obviated, and the MM ambiguities in road crossings can be greatly removed. Finally, an onsite MM test in Hong Kong is presented, and the test proves that the proposed MM algorithm provides long-time robust positioning performance. [ABSTRACT FROM AUTHOR]