Maximizing the utilization of grid components in operation is a major challenge for distribution system operators. In order to achieve this, assessment of the technical conditions of the equipment is required. The dielectric performance of insulation systems can be evaluated by the interpretation of Partial Discharge (PD) signals. Evaluation of the characteristics of these time-resolved signals is of highest importance. Conventional methods have several drawbacks, such as the complexity of the test setup and the settings as well as costs of the systems. Available PD measuring systems are not designed to face future challenges arising from Direct Current (DC)-infrastructure for energy distribution. To apply machine learning (ML) algorithms, typically the complete time signals have to be provided in order to extract features. In the present study, to allow a real-time processing at high signal resolutions, an automatically initializing algorithm was developed to separate noise and deterministic signals from each other. The PD or interference signals are available for extended signal processing such as clustering or classification. The implementation of this technology for Alternating Current (AC) and DC voltage applications was evaluated under practical conditions. Finally, an outlook on future applications and options using algorithms of ML in combination with the presented technology is given.