Use of Machine Learning for Partial Discharge Discrimination

Partial discharge (PD) measurements are an important tool for assessing the condition of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power systems. Wavelet analysis was applied to pre-process the obtained measurement data. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments indicate that this approach is applicable for use with field measurement data

A New Method to Improve the Sensitivity of Leak Detection in Self-Contained Fluid-filled Cables

A method of real-time detection of leaks for self-contained fluid-filled cables without taking them out of service has been assessed and a novel machine learning technique, i.e. support vector regression (SVR) analysis has been investigated to improve the detection sensitivity of the self-contained fluid-filled (FF) cable leaks. The condition of a 400 kV underground FF cable route within the National Grid transmission network has been monitored by Drallim pressure, temperature and load current measurement system. These three measured variables are used as parameters to describe the condition of the cable system. In the regression analysis the temperature and load current of the cable circuit are used as independent variables and the pressure within cables is the dependent variable to be predicted. As a supervised learning algorithm, the SVR requires data with known attributes as training samples in the learning process and can be used to identify unknown data or predict future trends. The load current is an independent variable to the fluid-filled system itself. The temperature, namely the tank temperature is determined by both the load current and the weather condition i.e. ambient temperature. The pressure is directly relevant to the temperature and therefore also correlated to the load current. The Gaussian-RBF kernel has been used in this investigation as it has a good performance in general application. The SVR algorithm was trained using 4 days data, as shown in Figure 1, and the optimized SVR is used to predict the pressure using the given load current and temperature information

Condition Monitoring of Power Cables

A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK electricity network operators has concentrated on the development of partial discharge data processing techniques that ultimately may allow continuous assessment of transmission asset health to be reliably determined

Assessing Ageing Condition of Mineral Oil-Paper Insulation by Polarization/Depolarization Current

Accurately assessing the ageing status of oil-paper insulation in transformer is essential and important. Polarization and Depolarization Current (PDC) technique is effective in assessing the condition of oil-paper insulation system. Though the PDC behaviour of mineral oil-paper insulation has been widely investigated, there is no report about how to make the quantitative analysis of mineral oil-paper insulation ageing condition by PDC. The PDC characteristics of mineral oil-paper insulation samples were investigated over the ageing period at 110°C. A new method for assessing the ageing condition of mineral oil-paper insulation by calculating the depolarization charge quantity was proposed. Results show that the depolarization charge quantity of mineral oil-paper insulation sample is very sensitive to its ageing condition. The stable depolarization charge quantity could be used to predict the ageing condition of mineral oil-paper insulation

CMBR Constraint on a Modified Chaplygin Gas Model

In this paper, a modified Chaplygin gas model of unifying dark energy and dark matter with exotic equation of state $p=B\rho-\frac{A}{\rho^{\alpha}}$ which can also explain the recent accelerated expansion of the universe is investigated by the means of constraining the location of the peak of the CMBR spectrum. We find that the result of CMBR measurements does not exclude the nonzero value of parameter $B$, but allows it in the range $-0.35\lesssim B\lesssim0.025$.Comment: 4 pages, 3 figure

First-principles study of native point defects in Bi2Se3

Using first-principles method within the framework of the density functional theory, we study the influence of native point defect on the structural and electronic properties of Bi$_2$Se$_3$. Se vacancy in Bi$_2$Se$_3$ is a double donor, and Bi vacancy is a triple acceptor. Se antisite (Se$_{Bi}$) is always an active donor in the system because its donor level ($\varepsilon$(+1/0)) enters into the conduction band. Interestingly, Bi antisite(Bi$_{Se1}$) in Bi$_2$Se$_3$ is an amphoteric dopant, acting as a donor when $\mu$$_e$$<$0.119eV (the material is typical p-type) and as an acceptor when $\mu$$_e$$>$0.251eV (the material is typical n-type). The formation energies under different growth environments (such as Bi-rich or Se-rich) indicate that under Se-rich condition, Se$_{Bi}$ is the most stable native defect independent of electron chemical potential $\mu$$_e$. Under Bi-rich condition, Se vacancy is the most stable native defect except for under the growth window as $\mu$$_e$$>$0.262eV (the material is typical n-type) and $\Delta$$\mu$$_{Se}$$<$-0.459eV(Bi-rich), under such growth windows one negative charged Bi$_{Se1}$ is the most stable one.Comment: 7 pages, 4 figure

The Effects of Halo Assembly Bias on Self-Calibration in Galaxy Cluster Surveys

Self-calibration techniques for analyzing galaxy cluster counts utilize the abundance and the clustering amplitude of dark matter halos. These properties simultaneously constrain cosmological parameters and the cluster observable-mass relation. It was recently discovered that the clustering amplitude of halos depends not only on the halo mass, but also on various secondary variables, such as the halo formation time and the concentration; these dependences are collectively termed assembly bias. Applying modified Fisher matrix formalism, we explore whether these secondary variables have a significant impact on the study of dark energy properties using the self-calibration technique in current (SDSS) and the near future (DES, SPT, and LSST) cluster surveys. The impact of the secondary dependence is determined by (1) the scatter in the observable-mass relation and (2) the correlation between observable and secondary variables. We find that for optical surveys, the secondary dependence does not significantly influence an SDSS-like survey; however, it may affect a DES-like survey (given the high scatter currently expected from optical clusters) and an LSST-like survey (even for low scatter values and low correlations). For an SZ survey such as SPT, the impact of secondary dependence is insignificant if the scatter is 20% or lower but can be enhanced by the potential high scatter values introduced by a highly correlated background. Accurate modeling of the assembly bias is necessary for cluster self-calibration in the era of precision cosmology.Comment: 13 pages, 5 figures, replaced to match published versio

Microwave intermodulation distortion of MgB2 thin films

The two tone intermodulation arising in MgB2 thin films deposited in-situ by planar magnetron sputtering on sapphire substrates is studied. Samples are characterised using an open-ended dielectric puck resonator operating at 8.8 GHz. The experimental results show that the third order products increase with the two-tone input power with a slope ranging between 1.5 and 2.3. The behaviour can be understood introducing a mechanism of vortex penetration in grain boundaries as the most plausible source of non linearities in these films. This assumption is confirmed by the analysis of the field dependence of the surface resistance, that show a linear behaviour at all temperatures under test.Comment: 13 pages, 3 figures; to be published in Appl. Phys. Let

Resonance NLS Solitons as Black Holes in Madelung Fluid

A new resonance version of NLS equation is found and embedded to the reaction-diffusion system, equivalent to the anti-de Sitter valued Heisenberg model, realizing a particular gauge fixing condition of the Jackiw-Teitelboim gravity. The space-time points where dispersion change the sign correspond to the event horizon, and the soliton solutions to the AdS black holes. The soliton with velocity bounded above describes evolution on the hyperboloid with nontrivial winding number and create under collisions the resonance states with a specific life time.Comment: Plain Tex, 12 pages, 6 figure

Emergent Universe with Exotic Matter in Brane World Scenario

In this work, we have examined the emergent scenario in brane world model for phantom and tachyonic matter. For tachyonic matter field we have obtained emergent scenario is possible for closed, open and at model of the universe with some restriction of potential. For normal scalar field the emergent scenario is possible only for closed model and the result is identical with the work of Ellis et al [2], but for phantom field the emergent scenario is possible for closed, open and at model of the universe with some restriction of potential