Automation and Optimisation of Wavelet Transform Techniques for Partial Discharge Denoising, and Pulse Shape Classification, in Power Plant

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Before high voltage plant fails there is generally a period when degradation of the insulation system occurs, this may be a number of years. The key to improving the assessment of the equipment condition and life expectancy lies in identifying and characterising the stages of degradation. It is widely recognised that the degradation phase, irrespective of the cause, results in small sparks being generated at the site(s) of degradation. These electric sparks are generally referred to as partial discharges(PD). The characteristics of the sparks are influenced by the materials and stresses at the fault site. Improvement in their detection and characterisation will provide information on the location, nature, form and extent of degradation.The current detection process is severely compromised in practical on-site testing. These PD pulses are extremely small and hence, irrespective of the particular strategy being applied to detect them(electrical or acoustic), detection equipment must be very sensitive. In the field, this makes it prone to the influence or external interference or 'noise' from the surrounding environment and electrical/mechanical infrastructure. At best, this results in data corruption and compromises the efficiency of the condition assessment. At worst, it stops the technique from being of any use as the 'noise' signal exceeds the level of partial discharge activity.To solve the problems associated with noise a number of methods have been tried such as: screening and filtering, the application of analogue band-pass filtering, matched filters, polarity discrimination circuitry, time-windowed methods and digital filters. Each of these is, however, applicable to only certain types of noiseIn a recent study the author compared the matched filter, the traditional filter and the Discrete Wavelet Transform (DWT) in PD measurement denoising and has proven DWT provides the best solution in practical measurement when strong noise is in presence. Furthermore, DWT is the only method which allows reconstruction of the PD pulse.Having evolved from the Fourier Transform(FT), WT is particularly designed to analyse transient, irregular and non-periodic signals. Ideally, if a wavelet can be selected to match the PD pulse shape, the PD pulse could be extracted from any strong noise signals. Though the WT generates more information than the FT, it is inherently more complex than the FT and involves procedures dependent on the shape of the signals to be extracted from noisy data, the record length and the sampling rate. Dr. Zhou in the Insulation Diagnostics Group at the GCU was the first to study the optimal selection of the most appropriate wavelets, the optimal number of levels and level-dependent thresholding algorithm for automatic PD pulse extraction from electrically noisy environments using DWT. This innovative work has been proved to be effective in a number of measurement platforms. However, the application of DWT still requires significant experience at the moment when pulses of different shapes exist. The proposed research is to build on the experience and success already gained at GCU and to develop a methodology which allows the DWT to be applied to various PD measurement systems irrespective of their mechanism and bandwidth for PD data denoising and PD pulse reconstruction and classification.The outcome of the proposed research will be algorithms which can identify all types of transient pulses contained in data under analysis and present them separately in time domain. This would allow the identification and classification of various PD activities from PD measurements and production of phi-q-n diagrams which, in conjunction with pulse shapes, provides significantly improved means for plant diagnosis.