Developing a Filtering Algorithm for Partial Discharge Location Approximation Using the Emitted Electromagnetic Signals of Corona Discharges

Abstract

The aim of this article is to create a novel filtering algorithm for the emitted electromagnetic signals of corona discharges, using the special time-domain features of the continuous wavelet transform. The first part of this article therefore focuses on the time-domain examination of these signals to find characteristic features that are compatible with the wavelet transform. The second part of this article utilizes these features to create and validate the filtering algorithm. This is an important step toward creating an accurate three-dimensional partial discharge location approximation system that could contribute to preventing and finding partial-discharge-related faults. In the presented research, needle-plane corona discharge sources with different geometries are tested at both ac and dc voltages.

Partial discharge detection and identification at low air pressure in noisy environment

“…In order to localise and detect PDs accurately, proper time domain filtering and signal processing techniques are necessary to be employed for the development of a PD source identification system. In [23], continuous wavelet transform (CWT) of the measured noisy corona PD signal has been calculated to highlight the arrival time of corona discharges. PD pulses have been extracted by removing the average of the measured signal to obtain non‐stationary PD pulses [24].…”