UHF diagnostic monitoring techniques for power transformers

This paper initially gives an introduction to ultra-high frequency (UHF) partial discharge monitoring techniques and their application to gas insulated substations. Recent advances in the technique, covering its application to power transformers, are then discussed and illustrated by means of four site trials. Mounting and installation of the UHF sensors is described and measurements of electrical discharges inside transformers are presented in a range of formats, demonstrating the potential of the UHF method. A procedure for locating sources of electrical discharge is described and demonstrated by means of a practical example where a source of sparking on a tap changer lead was located to within 15 cm. Progress with the development of a prototype on-line monitoring and diagnostic system is reviewed and possible approaches to its utilization are discussed. New concepts for enhancing the capabilities of the UHF technique are presented, including the possibility of monitoring the internal mechanical integrity of plant. The research presented provides sufficient evidence to justify the installation of robust UHF sensors on transformer tanks to facilitate their monitoring if and when required during the service lifetime

Thermodynamics of Random Ferromagnetic Antiferromagnetic Spin-1/2 Chains

Using the quantum Monte Carlo Loop algorithm, we calculate the temperature dependence of the uniform susceptibility, the specific heat, the correlation length, the generalized staggered susceptibility and magnetization of a spin-1/2 chain with random antiferromagnetic and ferromagnetic couplings, down to very low temperatures. Our data show a consistent scaling behavior in all the quantities and support strongly the conjecture drawn from the approximate real-space renormalization group treatment.A statistical analysis scheme is developed which will be useful for the search of scaling behavior in numerical and experimental data of random spin chains.Comment: 13 pages, 13 figures, RevTe

Crystal structure, impedance and broadband dielectric spectra of ordered scheelite-structured Bi(Sc1/3Mo2/3)O4 ceramic

Bi(Sc 1/3 Mo 2/3 )O 4 ceramics were prepared via solid state reaction method. It crystallized with an ordered scheelite-related structure (a = 16.9821(9) Å, b = 11.6097(3) Å, c = 5.3099(3) Å and β = 104.649(2)°) with a space group C12/C1, in which Bi 3+ , Sc 3+ and Mo 6+ are -8, -6 and -4 coordinated, respectively. Bi(Sc 1/3 Mo 2/3 )O 4 ceramics were densifiedat 915 °C, giving a permittivity (ε r ) 24.4, quality factor (Qf, Q = 1/dielectric loss, f = resonant frequency) ~48, 100 GHz and temperature coefficient of resonant frequency (TCF) -68 ppm/°C. Impedance spectroscopy revealed that there was only a bulk response for conductivity with activation energy (E a ) ~0.97 eV, suggesting the compound is electrically and chemically homogeneous. Wide band dielectric spectra were employed to study the dielectric response of Bi(Sc 1/3 Mo 2/3 )O 4 from 20 Hz to 30 THz. ε r was stable from 20 Hz to the GHz region, in which only ionic and electron displacive polarization contributed to the ε r

Estimates of hadron azimuthal anisotropy from multiparton interactions in proton-proton collisions at sqrt(s) = 14 TeV

We estimate the amount of collective "elliptic flow" expected at mid-rapidity in proton-proton (p-p) collisions at the CERN Large Hadron Collider (LHC), assuming that any possible azimuthal anisotropy of the produced hadrons with respect to the plane of the reaction follows the same overlap-eccentricity and particle-density scalings as found in high-energy heavy ion collisions. Using a Glauber eikonal model, we compute the p-p eccentricities, transverse areas and particle-multiplicities for various phenomenological parametrisations of the proton spatial density. For realistic proton transverse profiles, we find integrated elliptic flow v2 parameters below 3% in p-p collisions at sqrt(s) = 14 TeV.Comment: 17 pages, 9 figures. Very minor mods. Version to appear in EPJ-

Anomaly Analysis in Cleaning-in-Place Operations of an Industrial Brewery Fermenter

Analyzing historical data of industrial cleaning-in-place (CIP) operations is essential to avoid potential operation failures but is usually not done. This paper presents a three-level approach of analysis based on the CIP case of a brewery fermenter to describe how to analyze the historical data in steps for detecting anomalies. In the first level, the system is assessed before cleaning to ensure that the selected recipe and system are able to accomplish the task. In the second level, a multiway principal component analysis (MPCA) algorithm is applied to monitor the process variables online or post cleaning, with the purpose of locally detecting the anomalies and explaining the potential causes of the anomalous event. The third level analysis is performed after cleaning to evaluate the cleaning results. The implementation of the analysis approach has significant potential to automatically detect deviations and anomalies in future CIP cycles and to optimize the cleaning process

A Game Theoretic Approach To Learning Shape Categories and Contextual Similarities

Abstract. The search of a model for representing and evaluating the similarities between shapes in a perceptually coherent way is still an open issue. One reason for this is that our perception of similarities is strongly influenced by the underlying category structure. In this paper we aim at jointly learning the categories from examples and the similar-ity measures related to them. There is a chicken and egg dilemma here: class knowledge is required to determine perceived similarities, while the similarities are needed to extract class knowledge in an unsuper-vised way. The problem is addressed through a game theoretic approach which allows us to compute 2D shape categories based on a skeletal rep-resentation. The approach provides us with both the cluster information needed to extract the categories, and the relevance information needed to compute the category model and, thus, the similarities. Experiments on a database of 1000 shapes showed that the approach outperform other clustering approaches that do not make use of the underlying contextual information and provides similarities comparable with a state-of-the-art label-propagation approach which, however, cannot extract categories.

The COSINE-100 liquid scintillator veto system

This paper describes the liquid scintillator veto system for the COSINE-100 dark matter experiment and its performance. The COSINE-100 detector consists of eight NaI(Tl) crystals immersed in 2200 L of linear alkylbenzene-based liquid scintillator. The liquid scintillator tags between 65 and 75% of the internal 40K background in the 2–6 keV energy region. We also describe the background model for the liquid scintillator, which is primarily used to assess its energy calibration and threshold

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Measurement of the cosmic muon annual and diurnal flux variation with the COSINE-100 detector

We report measurements of annual and diurnal modulations of the cosmic-ray muon rate in the Yangyang underground laboratory (Y2L) using 952 days of COSINE-100 data acquired between September 2016 and July 2019. A correlation of the muon rate with the atmospheric temperature is observed and its amplitude on the muon rate is determined. The effective atmospheric temperature and muon rate variations are positively correlated with a measured effective temperature coefficient of αT = 0.80 ± 0.11. This result is consistent with a model of meson production in the atmosphere. We also searched for a diurnal modulation in the underground muon rate by comparing one-hour intervals. No significant diurnal modulation of the muon rate was observed