In this paper, the properties of AE signals originating from phenomena occurring during magnetization of ferromagnetic materials which are used to construct power transformer cores are presented. The AE signals in a selected power oil transformer were recorded and analyzed. The analysis included, i.e., time, frequency, and time-frequency analyses, calculations of amplitude distributions of the signals and defined AE descriptors, determination of the descriptor map on the side walls of transformers, as well as a detailed analysis of selected part of the signals. The maps of descriptors were analyzed in the frequency bands of 20–70 kHz, 70–100 kHz, and 100–200 kHz. The analysis of the properties of the signals was performed in time and frequency domains. Based on the analysis, there were identified the AE signals originating from the phenomena occurring during the core magnetization of a power oil transformer. To identify those phenomena, the maps of the ADC descriptor calculated in the band of 20–70 kHz when selecting the measurement points in which there were no AE sources from partial discharges were used. An analysis of magnetoacoustic emission signals in the bands of 70–100 kHz and 100–200 kHz was also performed. The analysis of the signal properties in such an extended frequency range allowed determining the properties of the magnetoacoustic signals coming from core sheets of power oil transformers.
This paper presents a method for estimation of core losses in banks of single phase power transformers that are subjected to an injected DC current such as geomagnetically induced currents (GIC). The main procedure of the core loss calculation is to obtain a magnetic flux density waveform in both time and location by using a novel algorithm based on 3D FEM inside the core and then to calculate the loss distribution based on loss separation theory. Also, a simple and effective method is proposed for estimation of losses of asymmetric minor loops by using combination of symmetric loops. The effect of DC biasing on core losses in single phase power transformers is investigated and the sensitivity of core type and material is evaluated. the results shows that DC current biasing could increase core losses up to 40 percent or even more.
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.