The article presents the results concerning the use of clustering methods to identify signals of acoustic emission (AE) generated by partial discharge (PD) in oil-paper insulation. The conducted testing featured qualitative analysis of the following clustering methods: single linkage, complete linkage, average linkage, centroid linkage and Ward linkage. The purpose of the analysis was to search the tested series of AE signal measurements, deriving from three various PD forms, for elements of grouping (clusters), which are most similar to one another and maximally different than in other groups in terms of a specific feature or adopted criteria. Then, the conducted clustering was used as a basis for attempting to assess the effectiveness of identification of particular PD forms that modelled exemplary defects of the power transformer’s oil-paper insulation system. The relevant analyses and simulations were conducted using the Matlab estimation environment and the clustering procedures available in it. The conducted tests featured analyses of the results of the series of measurements of acoustic emissions generated by the basic PD forms, which were obtained in laboratory conditions using spark gap systems that modelled the defects of the power transformer’s oil-paper insulation.
On-load tap changers (OLTC) are some of the main transformer elements that make voltage adjustment in a power network possible. Their failures often cause shutdowns of distribution transformers. The paper presents research work aimed at the assessment of the technical condition of OLTCs by the acoustic emission method (EA). This method makes the OLTC diagnosis possible without the need of disconnecting the transformer from the system. The measurements were taken in laboratory conditions. The influence on the operation non-concurrence of the power tap changer contacts on the AE registered signals has been investigated. The signals registered were subjected to analyses in the time and time-frequency domains. The result analysis in the time domain was carried out using the Hilbert transform and calculating characteristic times for the particular runs. A short-time Fourier transform was used for the assessment of results in the time-frequency domain.