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.
This paper presents a methodology for the calculation of the flux distribution in power transformer cores considering nonlinear material, with reduced computational effort. The calculation is based on a weak coupled multi-harmonic approach. The methodology can be applied to 2D and 3D Finite Element models. The decrease of the computational effort for the proposed approach is >90% compared to a time-stepping method at comparable accuracy. Furthermore, the approach offers a possibility for parallelisation to reduce the overall simulation time. The speed up of the parallelised simulations is nearly linear. The methodology is applied to a single-phase and a three-phase power transformer. Exemplary, the flux distribution for a capacitive load case is determined and the differences in the flux distribution obtained by a 2D and 3D FE model are pointed out. Deviations are significant, due to the fact, that the 2D FE model underestimates the stray fluxes. It is shown, that a 3D FE model of the transformer is required, if the nonlinearity of the core material has to be taken into account.
This paper presents comparative analysis of various acoustic signals expected during partial discharge (PD) measurements in operating power transformer. Main purpose of the paper is to yield relevant and reliable method to distinguish between various acoustic emission (AE) signals emitted by PD and other sources, with particular consideration of real-life results rather than laboratory simulations. Therefore, selected examples of real-life AE signals registered in seven different power transformers, under normal operation conditions, within few years are showed and analyzed. Five scenarios are investigated, which represent five types of AE sources: PD generated by artificial sources, and next four real-life sources (including PD in working transformer, oil flow, oil pumps and core). Several different signal processing methods are applied and compared in order to identify the PD signals. As a result, an energy patterns analysis based on the wavelet decomposition is found as the most reliable tool for identification of PD signals. The presented results may significantly support the process of interpretation of the PD measurement results, and may be used by field engineers as well as other researchers involved in PD analysis using AE method. Finally, observed properties also provide a solid basis for establishing or improving complete classification method based on the artificial intelligence algorithms.
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.
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