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

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

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/

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

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

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

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 university’s 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 university’s utility map shows the cable at X = 1.4 m, and by amplitude measurements taken with a hand-held magnetic field meter