Analysis of Natural Convection and High Voltage AC Cable Rating in Naturally Ventilated Tunnels

High voltage (HV) underground power cables are an important part of the power grid. In recent years, the necessity of optimization of power flow within the grid has increased significantly. The transition to renewable energy as well as the growth of electricity demand has resulted in high loads on the existing grid network. In order to optimize the ampacity of underground power cables, a good understanding of their thermal environment is needed. However, the occurrence and effect of wind flow in naturally ventilated cable tunnels has not been researched extensively. To further explore this phenomenon, two temperature sensors and a wind sensor were installed at an HVAC power cable tunnel site. The power cable site contains a three phase 2000 mm2 aluminum cable, rated for 132 kV, with cross-linked polyethylene (XLPE) insulation. The collection of temperature and wind data were compared to theoretical models to investigate whether the models could be used for future estimation of wind speed in cable tunnels. A validation of the IEC 60287-2-31 [1] model, for calculating the ampacity rating, was also explored. The wind sensor registered a maximum wind speed of 0.7 m/s at the tunnel exit, which coincided with the theoretical calculations, for naturally induced wind. The calculations were made using an equivalent diameter as the tunnel diameter. However, more data is needed to verify the model. The cable at the sensor site had a steady but low load. The load current was around 300 A, compared to the maximum permissible load of 985 A. A cable with a higher load current would have resulted in higher cable temperatures, leading to clearer wind data. The IEC model, used for ampacity and temperature calculations, correlated with the collected temperature data with an error of ca 20%, meaning that the IEC model is in good agreement with the collected data at this specific site. Thus the IEC model can be used to establish an ampacity rating for an HVAC cable laid in an underground tunnel. Furthermore, the calculation of naturally induced wind flow can be used as an indication of wind speed in cable tunnels, until further verified

Asset Management in Electricity Transmission Utilities: Investigation into Factors Affecting and their Impact on the Network

This thesis draws on techniques from Management Science and Artificial Intelligence to explore asset management in electricity transmission enterprises. In this research, factors that influence policies and practices of asset management within electricity transmission enterprises have been identified, in order to examine their interaction and how they impact the policies, practices and performance of transmission businesses. It has been found that, while there is extensive literature on the economics of transmission regulation and pricing, there is little published research linking the engineering and financial aspects of transmission asset management at a management policy level. To remedy this situation, this investigation has drawn on a wide range of literature, together with expert interviews and personal knowledge of the electricity industry, to construct a conceptual model of asset management with broad applicability across transmission enterprises in different parts of the world. A concise representation of the model has been formulated using a Causal Loop Diagram (CLD). To investigate the interactions between factors of influence it is necessary to implement the model and validate it against known outcomes. However, because of the nature of the data (a mix of numeric and non-numeric data, imprecise, incomplete and often approximate) and complexity and imprecision in the definition of relationships between elements, this problem is intractable to modelling by traditional engineering methodologies. The solution has been to utilise techniques from other disciplines. Two implementations have been explored: a multi-level fuzzy rule-based model and a system dynamics model; they offer different but complementary insights into transmission asset management. Each model shows potential to be used by transmission businesses for strategic-level decision support. The research demonstrates the key impact of routine maintenance effectiveness on the condition and performance of transmission system assets. However, performance of the transmission network, is not only related to equipment performance, but is a function of system design and operational aspects, such as loading and load factor. Type and supportiveness of regulation, together with the objectives and corporate culture of the transmission organisation also play roles in promoting various strategies for asset management. The cumulative effect of all these drivers is to produce differences in asset management policies and practices, discernable between individual companies and at a regional level, where similar conditions have applied historically and today

Bushing diagnosis using artificial intelligence and dissolved gas analysis

This dissertation is a study of artificial intelligence for diagnosing the condition of high voltage bushings. The techniques include neural networks, genetic algorithms, fuzzy set theory, particle swarm optimisation, multi-classifier systems, factor analysis, principal component analysis, multidimensional scaling, data-fusion techniques, automatic relevance determination and autoencoders. The classification is done using Dissolved Gas Analysis (DGA) data based on field experience together with criteria from IEEEc57.104 and IEC60599. A review of current literature showed that common methods for the diagnosis of bushings are: partial discharge, DGA, tan- (dielectric dissipation factor), water content in oil, dielectric strength of oil, acidity level (neutralisation value), visual analysis of sludge in suspension, colour of the oil, furanic content, degree of polymerisation (DP), strength of the insulating paper, interfacial tension or oxygen content tests. All the methods have limitations in terms of time and accuracy in decision making. The fact that making decisions using each of these methods individually is highly subjective, also the huge size of the data base of historical data, as well as the loss of skills due to retirement of experienced technical staff, highlights the need for an automated diagnosis tool that integrates information from the many sensors and recalls the historical decisions and learns from new information. Three classifiers that are compared in this analysis are radial basis functions (RBF), multiple layer perceptrons (MLP) and support vector machines (SVM). In this work 60699 bushings were classified based on ten criteria. Classification was done based on a majority vote. The work proposes the application of neural networks with particle swarm optimisation (PSO) and genetic algorithms (GA) to compensate for missing data in classifying high voltage bushings. The work also proposes the application of fuzzy set theory (FST) to diagnose the condition of high voltage bushings. The relevance and redundancy detection methods were able to prune the redundant measured variables and accurately diagnose the condition of the bushing with fewer variables. Experimental results from bushings that were evaluated in the field verified the simulations. The results of this work can help to develop real-time monitoring and decision making tools that combine information from chemical, electrical and mechanical measurements taken from bushings