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.

翻译：未检测到的局部放电（PD）是高压气体绝缘系统（GIS）中的关键安全问题。虽然交流电压下PD诊断技术已相当成熟，但直流电压下PD的分析仍是一个活跃的研究领域。这些研究的重点之一是对不同PD源进行分类，以便进行后续的深入分析。本文提出并分析了一种基于神经网络的方法，用于对HVDC GIS中由金属突出物和绝缘子表面导电颗粒引起的PD信号进行分类，该方法无需依赖脉冲序列分析特征。与现有方法不同，本文所提模型能够区分在负电位和正电位下获取的PD信号，同时还能泛化至未见过的运行电压倍数。此外，我们比较了时域和频域输入信号的性能，并探究了不同归一化方案对消除传感器与缺陷位置间自由空间路径损耗影响的效果。