Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System.

This paper addresses the critical challenge of detecting, separating, and classifying partial discharges in substations. It proposes two solutions: the first involves developing a signal conditioning system to reduce the sampling requirements for PD detection and increase the signal-to-noise ratio. The second approach uses machine learning techniques to separate and classify PD based on features extracted from the conditioned signal.

Design and Application of a Metamaterial Superstrate on a Bio-Inspired Antenna for Partial Discharge Detection through Dielectric Windows.

The adaptation of dielectric windows as metamaterial superstrate over a bio-inspired Printed Monopole Antenna (PMA) was evaluated in order to improve the detection sensitivity of Ultra High Frequency (UHF) sensors designed for Partial Discharge (PD) measurement. For this purpose, rectangular and circular Split Ring Resonators (SRR) structures were designed and evaluated aiming to achieve a metamaterial superstrate that improves the characteristics of the bio-inspired PMA as the gain, bandwidth, and radiation pattern. Measurements of the PMA with metamaterial superstrate were carried out in an anechoic chamber and compared to the simulations performed.

Bio-inspired Printed Monopole Antenna Applied to Partial Discharge Detection.

A new, bio-inspired printed monopole antenna (PMA) model is applied to monitor partial discharge (PD) activity in high voltage insulating systems. An optimized sensor was obtained by designing a PMA in accordance with the characteristics of the electromagnetic signal produced by PD. An ultra-wideband (UWB) antenna was obtained by applying the truncated ground plane technique.