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Electrical transmission and distribution networks are prone to short circuit faults since they span over long distances to deliver the electrical power from generating units to where the energy is required. These faults are usually caused by vegetation growing underneath bare overhead conductors, large birds short circuiting the phases, mechanical failure of pin-type insulators or even insulation failure of cables due to wear and tear, resulting in creepage current. Short circuit faults are highly undesirable for distribution network companies since they cause interruption of supply, thus affecting the reliability of their network, leading to a loss of revenue for the companies. Therefore, accurate offline fault location is required to quickly tackle the repair of permanent faults on the system so as to improve system reliability. Moreover, it also provides a tool to identify weak spots on the system following transient fault events such that these future potential sources of system failure can be checked during preventive maintenance. With these aims in mind, a novel fault location technique has been developed to accurately determine the location of short circuit faults in a distribution network consisting of feeders and spurs, using only the phase currents measured at the outgoing end of the feeder in the substation. These phase currents are analysed using the Discrete Wavelet Transform to identify distinct features for each type of fault. To achieve better accuracy and success, the scheme firstly uses these distinct features to train an Artificial Neural Network based algorithm to identify the type of fault on the system. Another Artificial Neural Network based algorithm dedicated to this type of fault then identifies the location of the fault on the feeder or spur. Finally, a series of Artificial Neural Network based algorithms estimate the distance to the point of fault along the feeder or spur. The impact of wind farm connections consisting of doubly-fed induction generators and permanent magnet synchronous generators on the accuracy of the developed algorithms has also been investigated using detailed models of these wind turbine generator types in Simulink. The results obtained showed that the developed scheme allows the accurate location of the short circuit faults in an active distribution network. Further sensitivity tests such as the change in fault inception angle, fault impedance, line length, wind farm capacity, network configuration and white noise confirm the robustness of the novel fault location technique in active distribution networks.

This thesis discusses aspects of design, implementation and theory of expert systems, which have been constructed in a novel way using techniques derived from several existing areas of Artificial Intelligence research. In particular, it examines the philosophical and technical aspects of combining techniques derived from the traditional rule-based methods for knowledge representation, with others taken from connectionist (more commonly described as Artificial Neural Network) approaches, into one homogenous architecture. Several issues of viability have been addressed, in particular why an increase in system complexity should be warranted. The kind of gain that can be achieved by such hybrid systems in terms of their applicability to general problem solving and ability to continue working in the presence of noise, are discussed. The first aim of this work has been to assess the potential benefits of building systems from modular components, each of which is constructed using different internal architectures. The objective has been to progress the state of knowledge of the operational capabilities of a specific system. A hybrid architecture containing multiple neural nets and a rule-based system has been designed, implemented and analysed. In the course of, and as an aid to the development of the system, an extensive simulation work-bench has been constructed. The overall system, despite its increased internal complexity provides many benefits including ease of construction and improved noise tolerance, combined with explanation facilities. In terms of undesirable features inherited from the parent techniques the losses are low. The project has proved successful in its stated aims and has succeeded in contributing a working hybrid system model and experimental results derived from the comparison of this new approach with the two, primary, existing techniques.

A new methodology was developed for flow regime identification in pipes. The method utilizes the pattern recognition abilities of Artificial Neural Networks and the unprocessed time series of a system-monitoring-signal. The methodology was tested with synthetic data from a conceptual system, liquid level indicating capacitance signals from a Horizontal flow system and with a pressure difference signal from a S-shape riser. The results showed that the signals that were generated for the conceptual system had all their patterns identified correctly with no errors whatsoever. The patterns for the Horizontal flow system were also classified very well with a few errors recorded due to original misclassifications of the data. The misclassifications were mainly due to subjectivity and due to signals that belonged to transition regions, hence a single label for them was not adequate. Finally the results for the S-shape riser showed also good agreement with the visual observations and the few errors that were identified were again due to original misclassifications but also to the lack of long enough time series for some flow cases and the availability of less flow cases for some flow regimes than others. In general the methodology proved to be successful and there were a number of advantages identified for this neural network methodology in comparison to other ones and especially the feature extraction methods. These advantages were: Faster identfication of changes to the condition of the system, inexpensive suitable for a variety of pipeline geometries and more powerful on the flow regime identification, even for transitional cases.

M.Ing. (Electrical And Electronic Engineering) / This dissertation describes the development of a system of Artificial Neural Networks that enables the incremental training of feed forward neural networks using supervised training algorithms such as back propagation. It is argued that incremental learning is fundamental to the adaptive learning behavior observed in human intelligence and constitutes an imperative step towards artificial cognition. The importance of developing incremental learning as a system of ANNs is stressed before the complete system is presented. Details of the development and implementation of the system is complemented by the description of two case studies. In conclusion the role of the incremental learning system as basis for further development of fundamental elements of cognition is projected.