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Undetected partial discharges (PDs) are a safety critical issue in highvoltage (HV) gas insulated systems (GIS). While the diagnosis of PDs under ACvoltage is well-established, the analysis of PDs under DC voltage remains anactive research field. A key focus of these investigations is theclassification of different PD sources to enable subsequent sophisticatedanalysis.  In this paper, we propose and analyze a neural network-based approach forclassifying PD signals caused by metallic protrusions and conductive particleson the insulator of HVDC GIS, without relying on pulse sequence analysisfeatures. In contrast to previous approaches, our proposed model candiscriminate the studied PD signals obtained at negative and positivepotentials, while also generalizing to unseen operating voltage multiples.Additionally, we compare the performance of time- and frequency-domain inputsignals and explore the impact of different normalization schemes to mitigatethe influence of free-space path loss between the sensor and defect location.

Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks