Parameters determination for the coupled electromagnetic transformer model, with particular reference to winding faults studies

In the past few years, a considerable research effort has been devoted to the development of transformer digital models in order to simulate its behaviour under transient and abnormal operating conditions. Although many three-phase transformer models have been presented in the literature, there is a surprisingly lack of studies regarding the incorporation of winding faults. This paper presents a coupled electromagnetic transformer model for the study of winding inter-turn short-circuits. Particular attention will be given to the model parameters determination, for both healthy and faulty operating conditions. Experimental and simulation test results are presented in the paper, demonstrating the adequacy of the model as well as the methodologies for the parameters determination.

A three-dimensional hydrodynamic model with generic vertical coordinate

A three-dimensional primitive equation model and its application to a tidal estuary is described. The model solves the primitive equations for incompressible fluids with Boussinesq and hydrostatic approximations. The discretization is based on the finite volume method and allows a general vertical coordinate. The computational code is implemented in such a way that different vertical coordinates can be used in different parts of the domain. The model was designed to be able to simulate the flow both in the open ocean and in coastal and estuarine zones and can be coupled in a simple way to ecological models. The model was implemented successfully in several estuarine and coastal areas. Results are show for the Sado estuary in Portugal to illustrate model accuracy and potential. Quantitative validation is based on field data (water levels and velocities) while qualitative verification is based on the analysis of secondary flows.

Disparity energy model with keypoint disparity validation

A biological disparity energy model can estimate local depth information by using a population of V1 complex cells. Instead of applying an analytical model which explicitly involves cell parameters like spatial frequency, orientation, binocular phase and position difference, we developed a model which only involves the cells’ responses, such that disparity can be extracted from a population code, using only a set of previously trained cells with random-dot stereograms of uniform disparity. Despite good results in smooth regions, the model needs complementary processing, notably at depth transitions. We therefore introduce a new model to extract disparity at keypoints such as edge junctions, line endings and points with large curvature. Responses of end-stopped cells serve to detect keypoints, and those of simple cells are used to detect orientations of their underlying line and edge structures. Annotated keypoints are then used in the leftright matching process, with a hierarchical, multi-scale tree structure and a saliency map to segregate disparity. By combining both models we can (re)define depth transitions and regions where the disparity energy model is less accurate.

Neural networks PID autotuning with real-time adaptation

Despite the developments in more sophisticated controllers, still the Proportional, Integral and Derivative (PID) controller is by far the controller most widely used in industry automation.