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The modeling of high frequency electromagnetic transients and the simulation of the voltage and the current distribution in the multi-winding traction transformer's windings due to these transient waves are very important. In the present article, in addition of presenting finite element models, the coupled field-circuit approach is proposed for the modeling of high frequency electromagnetic transients in a multi-winding traction transformer. The proposed method uses two-dimensional finite element models coupled with an external circuit to model the electromagnetic transient behavior of the multi-winding traction transformer. Afterwards, the results of the presented method have been compared with the results obtained from a complete three-dimensional finite element model as well as the detail model's results and the results are validated. Finally, the validated high-frequency model has been used to study the impulse response of the transformer. As shown, the proposed approach is a simple and fast method, and also has good accuracy in modeling of the impulse voltage distribution in the multi-winding traction transformer's windings.
 

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Today, differential relays are used in order to protect power transformers against all kinds of faults and events. Despite advances in relay fabrication technology, the detection and discrimination of different events is still one of the most important challenges for the protection engineers in this field. In this paper, an intelligent hybrid method has been proposed to detect and classify internal electrical faults, external faults while saturating Current Transformers (CTs) and inrush current in transformers. First, the internal and external fault currents and the inrush currents of power transformers are simulated by the Real-Time Digital Simulator (RTDS) and its software package (RSCAD). Then, the sampled signals in different events are transmitted to MATLAB software for detection and discrimination. At this stage, using the Bayesian Classifier method, which directly evaluates the training data information, external faults are separated from the other operating conditions of the transformer. Then, other events such as inrush current and internal electrical faults will be distinguished from each other by Decision Tree (DT) and Support Vector Machine (SVM) methods. The results show that the proposed intelligent hybrid protection method has the ability to detect and classify different disturbances in transformers in real time state with appropriate accuracy, which is one of the main innovations of this study compared to other published research.