Semi-classical limit of large fermionic systems at positive temperature

We study a system of $N$ interacting fermions at positive temperature in a confining potential. In the regime where the intensity of the interaction scales as $1/N$ and with an effective semi-classical parameter $\hbar=N^{-1/d}$ where $d$ is the space dimension, we prove the convergence to the corresponding Thomas-Fermi model at positive temperature.Comment: Convergence of states rewritten. Some references adde

Implementation of a novel online condition monitoring thermal prognostic indicator system

This research aims to develop a reliable and robust online condition monitoring thermal prognostic indicator system which will reduce the risk of failures in a Power System Network. Real-time measurements (weather conditions, temperature of the cable joints or terminations, loading demand) taken close to underground cable will update the prognostic simulation model. Anomalies of the measurements along the cable will be compared with the predicted ones hence indicating a possible degradation activity in the cable. The use of such systems within a power networks will provide a smarter way of prognostic condition monitoring in which you measure less and model more. The use of suggested thermal models will enable the power network operators to maximize asset utilization and minimize constraint costs in the system

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

Prognostic indication of power cable degradation

The reliability and the health performance of network assets are of a great interest due to power network operators. This project investigates methods of developing a prognostic capability for evaluating the health and long term performance of ageing distribution cable circuits. From the instant of installation and operation, the insulating materials of a cable will begin to age as a result of a combination of mechanical, thermal and electrical factors. Development of simulation models can significantly improve the accuracy of prognostics, allowing the targeting of maintenance and reduction of in service failures [1]. Real-time measurements taken close to underground cables can update the simulation models giving a more accurate prognostic model.Currently the project investigates a thermal prognostic simulation model which will predict the likely temperature impact on a cable at burial depth according to weather conditions and known loading. Anomalies of temperature measurements along the cable compared to predicted temperatures will indicate a possible degradation activity in a cable. An experimental surface trough has been set up where operation of power cables is simulated with a control system which is able to model any cable loading. The surface temperature of the cable is continuously monitored as well as the weather conditions such as solar radiation, soil moisture content, wind speed, humidity, rainfall and air-temperature<br/

Condition monitoring and prognostic indicators for network reliability

Large-scale investment in transmission and distribution networks are planned over the next 10-15 years to meet future demand and changes in power generation. However, it is important that existing assets continue to operate reliably and their health maintained. A research project is considering the increased use of simulation models that could provide accurate prognostics, targeting maintenance and reduce in service failures. Such models could be further refined with parameters obtained from on-line measurements at the asset. It is also important to consider the future development of the research agenda for condition monitoring of power networks and with colleagues from National Grid, PPA Energy and the Universities of Manchester and Strathclyde, the research team are preparing a Position Paper on this subject

Location of Partial Discharges within a Transformer Winding Using Principal Component Analysis

Partial discharge (PD) may occur in a transformer winding due to ageing processes or defects introduced during manufacture. A partial discharge is defined as a localised electric discharge that only partially bridges the dielectric insulator between conductors when the electric field exceeds a critical value. The presence of PD does not necessarily indicate imminent failure of the transformer but it is a serious degradation and ageing mechanism which can be considered as a precursor of transformer failure. PD might occur anywhere along the transformer winding and the discharge signal can propagate along the winding to the bushing and neutral to earth connections. As far as maintenance and replacement processes are concerned, it is important to identify the location of PD activity so any repair or replace decision is assured to be cost effective. Therefore, identification of a PD source as well as its location along the transformer winding is of great interest to both manufacturers and system operators. The wavelet transform is a mathematical function that can be used to decompose a PD signal into detail levels and an approximation. Wavelet filtering is often used to improve signal to noise ratio (SNR) of measured signals, but in this case it is used to identify the distribution of signal energies in both the time and frequency domains. This method produces a feature vector for each captured discharge signal. The use of principle component analysis (PCA) can compress this data into three dimensions, to aid visualisation. Data captured by sensors over hundreds of cycles of applied voltage can be analysed using this approach. An experiment (Figure 1) has been developed that can be used to create PD data in order to investigate the feasibility of using PCA analysis to identify PD source location

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

Effect of Cross-Linking on the Electrical Properties of LDPE and its Lightning Impulse Ageing Characteristics

Cross-linked polyethylene (XLPE) is commonly used within high voltage cable insulation. It has improved thermal and mechanical resistance compared to normal low density polyethylene (LDPE). However, the cross-linking process may also vary the electrical characteristics of the material. This paper investigates changes in electrical properties of one type of LDPE before and after cross-linking. The effective lightning resistance is also considered, as the application of repetitive lightning impulse overvoltages can be a factor in insulation material ageing of high voltage cables. The material was cross-linked using trigonox-145 peroxide with controlled concentration. Samples were moulded to have a Rogowski profile and gold coated to make sure that they are evenly electrically stressed. Obtained results show that there are reductions in both space charge injection and the permittivity of the material after it is cross-linked. The breakdown strength of the material was also improved. However, the samples studied are more susceptible to ageing due to lightning impulses

Electromagnetic field application to underground power cable detection

Before commencing excavation or other work where power or other cables may be buried, it is important to determine the location of cables to ensure that they are not damaged. This paper describes a method of power-cable detection and location that uses measurements of the magnetic field produced by the currents in the cable, and presents the results of tests performed to evaluate the method. The cable detection and location program works by comparing the measured magnetic field signal with values predicted using a simple numerical model of the cable. Search coils are used as magnetic field sensors, and a measurement system is setup to measure the magnetic field of an underground power cable at a number of points above the ground so that it can detect the presence of an underground power cable and estimate its position. Experimental investigations were carried out using a model and under real site test conditions. The results show that the measurement system and cable location method give a reasonable prediction for the position of the target cable