Empirical analysis of partial discharge data and innovative visualization tools for defect identification under DC stress
Saliha Abdul Madhar (Haefely AG, TU Delft - DC systems, Energy conversion & Storage)
Petr Mraz (Haefely AG)
Armando Rodrigo-Mor (TU Delft - DC systems, Energy conversion & Storage)
Rob Ross (TU Delft - DC systems, Energy conversion & Storage)
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Abstract
This paper presents several approaches to the analysis of partial discharge (PD) data. Three common defects namely corona, surface and floating electrode are studied with the goal of defect identification under DC stress conditions. One of the major concerns with DC-PD testing, is its non-repetitive/erratic pulse pattern. This paper, however, only deals with the repetitive stages of discharge that will allow the study of their resultant patterns and trends. Several unique features such as the formative trend in the probability plot of time between discharges for the three common defect types shows promise in the quest for defect identification under DC. Further, the paper also describes in which way a three-pulse PSA diagram cannot serve as a standalone figure and hence requires a change in perspective by either adding or reducing a dimension. The last part of the paper presents a test methodology to identify the discharge source based on various discharge features.
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