Advanced PD inference in on-field measurements. Part I: Noise rejection

Noise rejection, defect identification and degradation diagnosis in on-field partial discharge measurements are sought by industry, but hardly achieved in practice. This paper presents tools for automatic noise suppression in measurements performed by ultra wide band digitizers, able to record a large quantity of partial discharge (PD) pulse waveforms. Noise and PD signals are split in different classes on the basis of their shape by means of a fuzzy classifier. Tools used for establishing whether a given class of recorded signals is due to external noise or not are proposed. As an example, two kinds of noise are considered: random noise and rectifier-generated noise. A companion paper will explain how the same classification tools can be employed for the purpose of defect identification.