Multi-source Partial Discharge Signals Discrimination by Six Bandpass filters and DBSCAN Clustering


Firuzi K. , Vakilian M.

12th International Conference on the Properties and Applications of Dielectric Materials (ICPADM), Xian, China, 20 - 24 May 2018, pp.68-71 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icpadm.2018.8401079
  • City: Xian
  • Country: China
  • Page Numbers: pp.68-71
  • Keywords: Partial Discharge, Feature Extraction, Density-Based Spatial Clustering of Application with Noise density (DBSCAN)

Abstract

Partial discharge (PD) signals generated by defects in a transformer insulation can be captured through measurement instruments and they may be used, after preprocessing, to discriminate the PD Sources. Some of the artificial defect models, such as: corona, internal cavity and surface discharge in air are developed in the laboratory. These defect models are put in parallel under a high voltage stress. The PD signals stemmed from these sets of multiple PD sources are captured. In this paper six bandpass filters (with two MPD 600 devices) are used for feature extraction of these signals. For PD signals discrimination, the Density-Based Spatial Clustering of Applications with Noise density (DBSCAN) method is employed with two freedom parameters which resulted in an accurate discrimination of types of partial discharge sources.