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

TL;DR

This paper presents a neural network approach for classifying partial discharge signals in HVDC gas-insulated systems, capable of distinguishing different PD sources and generalizing across voltages without pulse sequence features.

Contribution

The study introduces a neural network model that classifies PD signals caused by different defects in HVDC GIS, generalizes to unseen voltages, and does not depend on pulse sequence analysis.

Findings

Model successfully discriminates PD sources at positive and negative potentials.

The approach generalizes to unseen operating voltage multiples.

Normalization schemes improve classification robustness.

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.