Development of interactive tutorial tool for simulation and identification of electrical machines and transformers

Abstract

The paper focuses on the development of a Matlab based interactive and tutorial tool for simulation and parameters identification of electrical machines, transformers and several other dynamic systems. The proposed software allows predicting the steady-state and dynamic performances of three-phase induction and synchronous machines, DC machines in both motor and generator modes, three-phase transformers and several other dynamic systems. A given machine under study is formatted in state space models. This allows performing various standard and non-standard tests. For linear and nonlinear deterministic machine models, linear and nonlinear deterministic predictors (Euler method and fourth order Runge-Kutta) are used, while the classical linear Kalman Filter (LKF) and Unscented Kalman Filter (UKF) are applied for the state estimation of linear and nonlinear stochastic machine models respectively. The availability of several optimization approaches for parameters identification experiences offers to users a great flexibility and opportunity to compare their robustness.