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

Efficient quantum transport simulation for bulk graphene heterojunctions

The quantum transport formalism based on tight-binding models is known to be powerful in dealing with a wide range of open physical systems subject to external driving forces but is, at the same time, limited by the memory requirement's increasing with the number of atomic sites in the scattering region. Here we demonstrate how to achieve an accurate simulation of quantum transport feasible for experimentally sized bulk graphene heterojunctions at a strongly reduced computational cost. Without free tuning parameters, we show excellent agreement with a recent experiment on Klein backscattering [A. F. Young and P. Kim, Nature Phys. 5, 222 (2009)].Comment: 5 pages, 3 figure

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

Cosmological dynamics of scalar fields with O(N) symmetry

In this paper, we study the cosmological dynamics of scalar fields with O(N) symmetry in general potentials. We compare the phase space of the dynamical systems of the quintessence and phantom and give the conditions for the existence of various attractors as well as their cosmological implications. We also show that the existence of tracking attractor in O(N) phantom models require the potential with $\Gamma<1/2$, which makes the models with exponential potential possess no tracking attractor.Comment: 9 pages, 4 figures; Replaced with the version to be published in Classical and Quantum Gravity. Reference adde

A Possible Late Time Λ\LambdaCDM-like Background Cosmology in Relativistic MOND Theory

In the framework of Relativistic MOND theory (TeVeS), we show that a late time background $\Lambda$CDM cosmology can be attained by choosing a specific $F(\mu)$ that also meets the requirement for the existence of Newtonian and MOND limits. We investigate the dynamics of the scalar field $\phi$ under our chosen $F(\mu)$ and show that the "slow roll" regime of $\phi$ corresponds to a dynamical attractor, where the whole system reduces to $\Lambda$CDM cosmology.Comment: Major revisions made; Matching the version to be published in IJMP

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

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