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Title: The application of ensemble neural networks for partial discharge pattern recognition
Author: Mas'ud, Abdullahi Abubakar
Awarding Body: Glasgow Caledonian University
Current Institution: Glasgow Caledonian University
Date of Award: 2013
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One technique of examining failures in the insulation of high voltage (HV) plant is through the evaluation of partial discharge (PD). PDs are electrical sparks that can deteriorate the insulation of HV equipment. However, once present, they become the principal mechanism of deterioration and can cause complete failure of the system leading to capital cost and economic consequences. As a consequence, developing techniques to characterize and classify PD is of profound importance to condition monitoring engineers. Indeed, since the nature, form and characteristics of PD have been widely investigated and in many ways established, it is vital to determine novel techniques that can effectively classify PD patterns and give a reliable assessment of the nature of the PD fault. In this thesis, enhanced PD pattern recognition tools are developed. The strategy concentrates for the first time on the application of ensemble neural networks (ENNs) to classify PD statistical patterns. The capability of the ENN to distinguish PD patterns has been extensively investigated and its performance compared with the widely applied single neural network (SNN). The ENN is shown to be more robust and generally demonstrates improved classification potential over the SNN in classifying PD fault statistical features and their progressive degradation. The ENN can also discriminate PD patterns between arrangements of one or 2 voids, different point-to-earth oil-gap discharges and angular positioning of the points on pressboard. Finally, this thesis investigates for the first time the influence on the SNN and ENN of phase resolution (PR) and amplitude bin (AB) size of the cp - q - n (phase-amplitude-number) statistical fingerprints. The result shows that there is apparent statistical distinction for different PR and AB sizes on some of the statistical cp - q - n distributions. Additionally, the ENN and SNN outputs can change depending on training and testing with different PR and AB sizes and that an optimised PR or AB may be shown to exist.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available