The density based segmentation algorithm for interpreting partial discharge data

Partial Discharge (PD) signal interpretation has become an essential tool for reliable quality assessment of insulation systems in HV electric machines. Automated classification and recognition of the different types of faults occurring within the insulation system is an important open research problem in this field. In this work, we propose a new approach for the interpretation of PD data. A novel segmentation algorithm is developed to segment PD patterns into their main connected components, with the goal of using these segments later on, to extract distinctive features necessary for fault identification and classification. Several segmentation results are reported along with examples illustrating possible usage in fault identification.