  Undetected partial discharges (PDs) are a safety critical issue in high
voltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under AC
voltage is well-established, the analysis of PDs under DC voltage remains an
active research field. A key focus of these investigations is the
classification of different PD sources to enable subsequent sophisticated
analysis.
  In this paper, we propose and analyze a neural network-based approach for
classifying PD signals caused by metallic protrusions and conductive particles
on the insulator of HVDC GIS, without relying on pulse sequence analysis
features. In contrast to previous approaches, our proposed model can
discriminate the studied PD signals obtained at negative and positive
potentials, while also generalizing to unseen operating voltage multiples.
Additionally, we compare the performance of time- and frequency-domain input
signals and explore the impact of different normalization schemes to mitigate
the influence of free-space path loss between the sensor and defect location.
