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Generalizable Classification of UHF Partial Discharge Signals in Gas-Insulated HVDC Systems Using Neural Networks
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  • Steffen Seitz ,
  • Christopher Lindenberg ,
  • Ronald Tetzlaff ,
  • Thomas Götz ,
  • Stephan Schlegel
Steffen Seitz
Technische Universität Dresden

Corresponding Author:[email protected]

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Christopher Lindenberg
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Ronald Tetzlaff
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Thomas Götz
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Stephan Schlegel
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Abstract

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.