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.

Three-phase, three-limb, steady-state transformer model

This paper presents the development and implementation of a digital simulation model of a threephase, three-leg, three-winding power transformer. The proposed model, implemented in MATLAB environment, is based on the simultaneous analysis of both magnetic and electric lumped-parameters equivalents circuits, and it is intended to study its adequacy to incorporate, at a later stage, the influences of the occurrence of windings interturn short-circuit faults. Both simulation and laboratory tests results, obtained so far, for a three-phase, 6 kVA transformer, demonstrate the adequacy of the model under normal operating conditions.

Model comparison for temperature estimation inside buildings

This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.