Abstract

The insulation systems of equipment, cables, and electrical machines subjected to high voltage are continuously exposed to multiple aging mechanisms of electrical, mechanical, thermal, and environmental type. Normally, a failure in the material under these conditions does not occur immediately, but over time, different degradation processes emerge and evolve, progressively deteriorating the insulation, until breakdown occurs, and consequently, the asset finishes its operation with a catastrophic failure. In this context, partial discharge (PD) measurement can be considered as one of the best indicators when diagnosing the status of much electrical equipment in operation. However, the simultaneous presence of noise sources or multiple PD sources can generate important difficulties in identifying the type or types of sources measured. These practical limitations can be solved if, prior to the identification, a separation process is carried out, which allows classifying the different sources acting over the equipment being monitored. Once the sources separation is executed, the subsequent identification and diagnosis process can be carried out more easily. In this article, we present a novel separation technique based on the temporal and spectral behavior of the PD signals. For this technique, the separation of sources is carried out through three different parameters. Two of them are based on the peakedness of PD signals and electrical noise, and the third parameter is associated with their spectral content. Based on these parameters, a 3-D separation map is established, representing in clusters each source captured by the sensors.