On-line condition monitoring of transition assets

There are a number of medium voltage (MV) power distribution cable networks worldwide that are constructed predominantly of mass impregnated paper cables - London being one of these. Paper insulated lead covered (PILC) cables were extensively laid in the 50s and 60s before the introduction of cheaper polymeric alternatives that were sufficiently reliable. The current operational state of these networks has shown a gradual increase in failure rates of the previously reliable paper cables that are drawing to the end of their expected design life. Utilities are faced with the prospect of the impending failure of large sections of their prized asset and are keen to develop tools to better understand the health of their hardware. The analysis of partial discharge (PD) signals produced by the cables has been identified as a economically viable option to provide continuous condition monitoring of PILC cable circuits. Clearly, a comprehensive understanding of how PD activity relates to the various failure mechanisms exhibited by cable circuits in the field is required. A recently published technique for PD source discrimination was coupled with an understanding of the experiment and applied to the experiment data to isolate the signals specific to each degradation mechanism [1]. This technique has been applied to both rotation machines and transformer systems with promising results. PD signal discrimination is seen as the first step towards an autonomous condition monitoring futur

On-line partial discharge analysis of transmission and distribution assets

It is becoming increasingly clear that methodologies for PD classification based on standard laboratory experimental data are not readily applicable when assessing online PD data measured in the field. It is not just that field data is corrupted by noise and disturbance, but also the significant differences between typical laboratory experiments to generate PD data and the generation of PDs in high voltage plant due to degradation of the insulation system. In this paper, the use of nonlinear time-series analysis on field data is shown to yield useful information, methods involving dimension reduction techniques are shown to allow identification of different sources and finally a method for designing standard finite impulse response filters that approximate the nonlinear analytical approach and are easy to implement in condition monitoring systems are discussed

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/

Thermal performance of high voltage power cables

The UK high voltage electricity transmission network continues to face annual rises in demand, with ever greater volumes of power supplied to load centres throughout the country. To operate this network effectively, it is vital to accurately calculate the maximum allowable electric current which can be safely carried by each component in the power system. In high voltage power cables, this limit is defined by the maximum operating temperature of the cable insulation. Specify this current rating to be too low and the cable asset will never be used to its full potential; conversely setting the rating to be too high risks damage to the asset as the excessive heating can cause premature failure. Thus the rating calculation must be optimised to maintain security of supply by minimising the risk of cable failure, while also maximising the returns from capital investment on the power network. This project has employed a variety of mathematical techniques to improve the methods by which current ratings are calculated. Modern computational techniques such as finite element analysis (e.g Figure 1) and computational fluid dynamics are used to create more advanced circuit rating techniques. These have been compared and refined with input gained from field data. By eliminating simplifications from existing methods, it has been possible to identify ways of increasing the utilisation of the existing network. In addition the new techniques allow examination of the potential benefits of future developments in cable technology. Benefits are being derived from this work on both a day to day and strategic planning levels. For instance, by re-evaluating the current rating method for cables installed in tunnels, it has proved possible to consider the benefits from co-locating more cables in one tunnel to best use these expensive assets. The application of this method has allowed the quantification of the benefits which might be available from next generation cable technologies, enabling the prioritisation of future research effort in cable materials. Upon completion, the knowledge gained from this work is to be used to revise the international standard on calculating current ratings in cable tunnels. Techniques such as these underpin the concept of smart grids with improved operational flexibility and capability. Simultaneously the requirement to build expensive new components into the network is limited, whilst still meeting the need to supply ever increasing volumes of power across the country

Method for rating power cables buried in surface troughs

An alternative method is detailed by which the ambient temperature parameter as applied to the calculation of ratings of cables buried in surface trough installations can be determined. Improvement in the accuracy of cable rating calculations will allow greater utilisation of the cable asset and assist for example in the planning of system outages for maintenance work. The proposed model calculates the temperature at the cable burial depth based on measurements of solar radiation, windspeed and air temperature. The model is based on physical laws rather than empirical approaches that have been shown to be generally conservative in application. Results based on weather data monitored over a two-year period show that the ambient temperature of the soil at cable depth can be accurately determined and the model provides a significant improvement on existing methods

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

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

Investigation into the formation of charge packets in polyethylene: experiment and simulation

The phenomenon of charge packet has been reported in polymeric insulation materials under the application of dc electric fields in recent decades. It is noted that such charge packets could lead to substantial modification of local electric stress, which increases the possibility of failure of insulating materials. The physics of charge packets has not yet been revealed clearly. In this paper, the dynamics of positive charge packets in polyethylene is observed using the pulsed electro-acoustic technique. Negative differential mobility of positive charge carrier is found, which is believed to be crucial to the formation of charge packets. This negative differential mobility is introduced into a bipolar charge transport model to simulate the packet-like space charge in polymers. Simulation results show that not only the negative differential mobility but also weaker trapping characteristic are required to generate a positive charge packet in polyethylene under dc stress believed to be crucial to the formation of charge packets. This negative differential mobility is introduced into a bipolar charge transport model to simulate the packet-like space charge in polymers. Simulation results show that not only the negative differential mobility but also weaker trapping characteristic are required to generate a positive charge packet in polyethylene under dc stress

Partial discharge testing of defective three-phase PILC cable under rated conditions

The ability to accurately monitor the health of power distribution plant is a very attractive prospect for utility companies. This capability would provide a system that engineers could use to assess the real-time state of the network. Analysis of the data produced could allow for more informed decisions to be made in the areas of asset replacement and maintenance scheduling amongst others. It is widely accepted that partial discharge activity is linked with the electrical ageing/degradation of high voltage equipment. Work at Southampton is focused on obtaining a better understanding of the characteristics and trends of partial discharge events associated with medium voltage cables under, 'real life' conditions. An experiment has been developed that allows for service conditions to be applied to defective paper insulated lead covered cable samples. The samples under investigation were exposed to mechanical damage designed to replicate typical problems found on an active circuit. Partial discharge measurement was undertaken during the stressing process

A new method for automatic Multiple Partial Discharge Classification

A new wavelet based feature parameter have been developed to represent the characteristics of PD activities, i.e. the wavelet decomposition energy of PD pulses measured from non-conventional ultra wide bandwidth PD sensors such as capacitive couplers (CC) or high frequency current transformers (HFCT). The generated feature vectors can contain different dimensions depending on the length of recorded pulses. These high dimensional feature vectors can then be processed using Principal Component Analysis (PCA) to map the data into a three dimensional space whilst the first three most significant components representing the feature vector are preserved. In the three dimensional mapped space, an automatic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then applied to classify the data cluster(s) produced by the PCA. As the procedure is undertaken in a three dimensional space, the obtained clustering results can be easily assessed. The classified PD sub-data sets are then reconstructed in the time domain as phase-resolved patterns to facilitate PD source type identification. The proposed approach has been successfully applied to PD data measured from electrical machines and power cables where measurements were undertaken in different laboratories