407 research outputs found
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
Towards Recyclable Insulation Materials for High Voltage Cables
The preferred material for modern extruded high voltage transmission cables is cross-linked polyethylene (XLPE). This material has excellent thermo-mechanical and dielectric properties, however it is not easily recycled at end of use, raising questions as to its long term sustainability [1]. Therefore research work at Southampton has sought to identify suitable recyclable alternatives to XLPE. Such candidate materials need to have low temperature flexibility and high temperature mechanical stability combined with a sufficiently high electrical breakdown strength
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
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
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
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
Detection and Location of Underground Power Cable using Magnetic Field Technologies
The location of buried underground electricity cables is becoming a major engineering and social issue worldwide. Records of utility locations are relatively scant, and even when records are available, they almost always refer to positions relative to ground-level physical features that may no longer exist or that may have been moved or altered. The lack of accurate positioning records of existing services can cause engineering and construction delays and safety hazards when new construction, repairs, or upgrades are necessary. Hitting unknown underground obstructions has the potential to cause property damage, injuries and, even deaths. Thus, before commencing excavation or other work where power or other cables may be buried, it is important to determine the location of the cables to ensure that they are not damaged during the work. This paper describes the use of an array of passive magnetic sensors (induction coils) together with signal processing techniques to detect and locate underground power cables. The array consists of seven identical coils mounted on a support frame; one of these coils was previously tested under laboratory conditions, and relevant results have been published in [1]. A measurement system was constructed that uses a battery powered data acquisition system with two NI 9239 modules connected to the coil array, and controlled by a laptop. The system is designed to measure the magnetic field of an underground power cable at a number of points above the ground. A 3 by 3 m test area was chosen in one of our campus car parks. This area was chosen because the universityâs utility map shows an isolated power cable there. Measurements were taken with the array in 16 different test positions, and compared with the values predicted for a long straight horizontal cable at various positions. Finally, error maps were plotted for different Z-coordinate values, showing the minimum fitting error for each position in this plane. One such map is shown in Figure 1; the low error values of 4-5% give a high degree of confidence that most of the measured signal is due to a cable near to these positions. This view is supported by the fact that the universityâs utility map shows the cable at X = 1.4 m, and by amplitude measurements taken with a hand-held magnetic field meter
Electrical Treeing in Silicone Rubber
Electrical treeing has been widely studied in a range of polymeric materials. In these investigations, the morphology and PD patterns associated with the growth of electrical trees in a model transparent silicone rubber were investigated using a new system recently developed at Southampton. With increasing voltage the trees became more complex in appearance but nevertheless grow more rapidly. As the tree evolves the PD pattern becomes more intense which may provide a method of monitoring the extent of treeing in opaque samples. Raman studies indicate that treeing and breakdown channels are hollow, carbonaceous entities, a finding consistent with other studies
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