The PRPDA (Phase Resolved Partial Discharges Acquisition) systems usually employ 8 bits mathrm A/mathrm D converters. Avoiding the converter saturation, it has been necessary, during the measurements, to set the reference voltage on the expected maximum PD signals magnitude. There is an amplitude threshold below which PD signals are not sufficiently quantized to let the separation among PD signals relevant to different defects and noise using automatic recognition algorithms. Therefore, it is necessary to employ PD acquisition systems that exploit more bits for the quantization in order to better distinguish the PD signals from the environmental noise. In this study, PDs and noise measurements have been performed considering for the quantization 8 bit and 12 bit acquisition systems. The efficiency of the recognizing method on the two datasets has been compared and evaluated. The considered algorithm is based upon the Equivalent Time - Equivalent Bandwidth (TW) projection of the data in order to allow a simple clustering in a 2D space.
Comparison between PD Acquisition System Measurements Using Different Number of Bits for the Quantization
Guastavino, F.;GIANOGLIO, CHRISTIAN;Torello, E.;Cordano, D.;BRUZZONE, ANDREA
2018-01-01
Abstract
The PRPDA (Phase Resolved Partial Discharges Acquisition) systems usually employ 8 bits mathrm A/mathrm D converters. Avoiding the converter saturation, it has been necessary, during the measurements, to set the reference voltage on the expected maximum PD signals magnitude. There is an amplitude threshold below which PD signals are not sufficiently quantized to let the separation among PD signals relevant to different defects and noise using automatic recognition algorithms. Therefore, it is necessary to employ PD acquisition systems that exploit more bits for the quantization in order to better distinguish the PD signals from the environmental noise. In this study, PDs and noise measurements have been performed considering for the quantization 8 bit and 12 bit acquisition systems. The efficiency of the recognizing method on the two datasets has been compared and evaluated. The considered algorithm is based upon the Equivalent Time - Equivalent Bandwidth (TW) projection of the data in order to allow a simple clustering in a 2D space.File | Dimensione | Formato | |
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