Résultats de recherche pour « Phase Identification »

Phase identification is a process to determine which of the three phases a particular house is connected to. The state-of-the-art identification methods usually exploit smart metering data. However, the data sets are not always available and the major challenge is hence to identify phases with incomplete data set. This paper proposes a novel spectral and saliency analysis identification method to overcome this hurdle. Spectral analysis is first performed to extract the high-frequency features from the incomplete data. Saliency analysis is then adopted to extract salient features from the variations of high-frequency loads in the time domain. Correlation analysis between customer features and the phase features is used to determine customers' phase connectivity. The method is executed iteratively until all customers with smart meters have been allocated to a specific phase or no salient features can be found. It is validated against real data from over 6000 smart meters in Ireland and achieves an accuracy of over 93% with only 10% smart meter penetration ratio in a 100-household network.

Over the last three years, a major international effort has been made by the Sub-Commission on Earthquake Algorithms of the International Association of Seismology and the Physics of the Earth's Interior (IASPEI) to generate new global traveltime tables for seismic phases to update the tables of Jeffreys & Bullen (1940). The new tables are specifically designed for convenient computational use, with high-accuracy interpolation in both depth and range. The new iasp91 traveltime tables are derived from a radially stratified velocity model which has been constructed so that the times for the major seismic phases are consistent with the reported times for events in the catalogue of the International Seismological Centre (ISC) for the period 1964–1987. The baseline for the P-wave traveltimes in the iasp91 model has been adjusted to provide only a small bias in origin time for well-constrained events at the main nuclear testing sites around the world. For P-waves at teleseismic distances, the new tables are about 0.7s slower than the 1968 P-tables (Herrin 1968) and on average about 1.8-1.9 s faster than the Jeffreys & Bullen (1940) tables. For S-waves the teleseismic times lie between those of the JB tables and the results of Randall (1971). Because the times for all phases are derived from the same velocity model, there is complete consistency between the traveltimes for different phases at different focal depths. The calculation scheme adopted for the new iasp91 tables is that proposed by Buland & Chapman (1983). Tables of delay time as a function of slowness are stored for each traveltime branch, and interpolated using a specially designed tau spline which takes care of square-root singularities in the derivative of the traveltime curve at certain critical slownesses. With this representation, once the source depth is specified, it is straightforward to find the traveltime explicitly for a given epicentral distance. The computational cost is no higher than a conventional look-up table, but there is increased accuracy in constructing the traveltimes for a source at arbitrary depth. A further advantage over standard tables is that exactly the same procedure can be used for each phase. For a given source depth, it is therefore possible to generate very rapidly a comprehensive list of traveltimes and associated derivatives for the main seismic phases which could be observed at a given epicentral distance.

The task of determining the phase connection of customers, known as phase identification, is crucial to obtain accurate distribution system models. This paper starts with a thorough literature review of the existing phase identification methods, which are broadly divided into three categories: hardware-based, real power-based, and voltage-based methods. This is followed by multiple case studies assessing the accuracy of six real power- and voltage-based phase identification algorithms on four realistic distribution test systems. Synthetic load profiles along with network models are used to quantify accuracy of each method for different scenarios: varying advanced metering infrastructure (AMI) coverage, number of initially mislabeled customer phases, number of available samples, and measurement noise. A case study using a real AMI data set, including field verification, is also provided. Finally, several aspects key to accurate phase identification are discussed in detail.

Electron energy-loss spectroscopy measurements have been made on carbon and BN polymorphs, as well as on a number of borides, carbides and nitrides present in sintered cBN based compacts.The similarity in crystal symmetry between carbon and the isotructural BN system results in spectral similarities in both the low-loss features and electron energy-loss near-edge structure associ- ated with the particular core-loss ionization edges.ELNES features hold information on the nearest neighbour environment of the excited atom in a given phase.It is shown that the analysis of these features provides characteristic coordination fingerprints for the identification of carbon and BN al- lotropes.Similarly, the comparison of ELNES features associated with B-K, C-K and N-K ionization edges provides fingerprints for the identification of B in TiB2 and B4C, C in TiC and B4C, and N in AlN, TiN and BN, respectively.An attempt is made to quantify the fraction of sp3 bonding in diamond-like carbon and cBN based cutting tool material from the relative intensity of the 03C0 reso- nance in C-K and B-K ELNES, respectively.

Introduction 16.1.1General remarks 16.1.2Historical overview 16.2 Phase and event detection 16.3 Automated P-onset determination 16.3.1The Allen picker 16.3.2The Baer and Kradolfer picker 16.3.3P-onset determination using Higher Order Statistic 16.3.4The AR-AIC picker 16.3.5Discussion of presented P-picking algorithms 16.4 Automated S-onset determination 16.5 Automated quality assessment, phase identification, and outlier detection 16.5.1 Quality assessment of phase times 16.5.2Phase identification 16.5.3Post-picking and outlier detection 16.6 Practical considerations: Implementation, calibration, and pitfalls of automatic detection and picking procedures 16.6.1Calibration and test of automatic pickers 16.6.2Pre-processing: wave filters 16.6.3Pre-processing: waveform quality Appendix

Backscattered electron Kikuchi patterns (BEKP) suitable for crystallographic phase analysis can be collected in the scanning electron microscope (SEM) with a newly developed charge coupled device (CCD) based detector. Crystallographic phase identification using BEKP in the SEM is unique in that it permits high magnification images and BEKPs to be collected from a bulk specimen. The combination of scanning electron microscope (SEM) imaging, BEKP, and energy dispersive x-ray spectrometry holds the promise of a powerful new tool for materials science.

This paper proposes a novel phase identification method for distribution networks where phases can be severely unbalanced and insufficiently labeled. The analysis approach draws on data from high-precision phasor measurement units (micro-synchrophasors or uPMUs) for distribution systems. A key fact is that time-series voltage phasors taken from a distribution network show specific patterns regarding connected phases at measurement points. The algorithm is based on analyzing cross-correlations over voltage magnitudes along with phase angle differences on two candidate phases to be matched. If two measurement points are on the same phase, large positive voltage magnitude correlations and small voltage angle differences should be observed. The algorithm is initially validated using the IEEE 13-bus model, and subsequently with actual uPMU measurements on a 12-kV feeder.

Four phases are observed in superconducting Bi-Ca-Sr-Cu-O samples. The superconducting phase, with onset temperature near 120 K, is a 15.4-A\r{}-layered compound with composition near ${\mathrm{Bi}}_{2}$${\mathrm{Ca}}_{1}$${\mathrm{Sr}}_{2}$${\mathrm{O}}_{9}$ and an A-centered orthorhombic unit subcell 5.41\ifmmode\times\else\texttimes\fi{}5.44\ifmmode\times\else\texttimes\fi{}30.78 A${\r{}}^{3}$. X-ray diffraction and electron microscopy data are consistent with a structure of alternating perovskite and ${\mathrm{Bi}}_{2}$${\mathrm{O}}_{2}$ layers. High-resolution transmission electron microscopy images reveal a b-axis superstructure of 27.2 A\r{}, numerous (001) stacking faults, and other defects.

Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.