Effect of acquisition parameters on equivalent time and equivalent bandwidth algorithms for partial discharge clustering. (June 2017) - Rodrigo Mor, A.; Castro Heredia, L.C.; Muñoz, F.A.
Highlights: The classification map, quoted in Standards, is affected by the signal to noise ratio. Variation in acquisition parameters affects clusters in the classification map. PD sources are clustered only if the signals have a high signal to noise ratio. When noise is high, diverse PD sources clusters tend to merge into a single cluster. Abstract: The acquisition parameters of an unconventional Partial Discharge (PD) measuring system affect the way the PD pulses are recorded and in turn, the results of the data processing. The noise based on the oscilloscope's vertical resolution is a feature of the sampled signal that is always present when a digital acquisition system is used. In PD unconventional systems, several parameters such as the sampling frequency Fs, the acquisition time T, the number of samples N and the vertical resolution VR of the digitizer result in a wide oscilloscope-based noise variation, that could be quantified by the signal to noise ratio (snr). The classification map is a tool that came available with the development of unconventional systems, that due to their wide bandwidth are able to resolve PD pulses in time and apply clustering techniques for PD source separation. The equivalent time Teq and equivalent bandwidth Weq, used to plot the classification map, attempts to extract features of the PD pulses to form clusters so that classification of sources can be achieved. The classification map is based on the ability of separating PD sources by resorting to the parameters Teq and Weq, that are believed to show significant differences for distinct PD sources, while they are clearly consistent for the same source. This paper conducts a set of theoretical analysis and laboratory measurements to evaluate the influence of the oscilloscope-based noise on the results of Teq and Weq . The results proved that the classification map is heavily influenced by the signal to noise ratio.