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.
The subject presented in this paper refers to measurements and assessment of the corrected sound pressure level values (noise) occurring around a medium-power transformer. The paper presents the values of noise accompanying the operation of the power object before and after its modernization, which consisted in repeated core pressing and replacement of the cooling system. The main aim of the research work was the assessment of the influence of the repair work on the noise level emitted into the environment.
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.
A transformer is an important part of power transmission and transformation equipment. Once a fault occurs, it may cause a large-scale power outage. The safety of the transformer is related to the safe and stable operation of the power system. Aiming at the problem that the diagnosis result of transformer fault diagnosis method is not ideal and the model is unstable, a transformer fault diagnosis model based on improved particle swarm optimization online sequence extreme learning machine (IPSO-OS-ELM) algorithm is proposed. The improved particle swarmoptimization algorithm is applied to the transformer fault diagnosis model based on the OS-ELM, and the problems of randomly selecting parameters in the hidden layer of the OS-ELM and its network output not stable enough, are solved by optimization. Finally, the effectiveness of the improved fault diagnosis model in improving the accuracy is verified by simulation experiments.