A new equivalent model of the induction motor with turn to turn fault on one phase has been developed. This model has been used to establish two schemes to estimate the severity of the short circuit fault. In the first scheme, the faulty model is considered as an observer, where a correction of an error between the measured and the estimated currents is the kernel of the fault severity estimator. However, to develop the second method, the model was required only in the training process of an artificial neural network (ANN). Since stator faults have a signature on symmetrical components of phase currents, the magnitudes and angles of these components were used with the mean speed value as inputs of the ANN

A model reference adaptive system (MRAS) based speed estimator for sensorless induction motor (IM) drive is proposed in this paper. The MRAS is formed with flux rotor and the estimated stator current vector. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. The performance of the estimator under regeneration is an important aspect, which is studied in this paper through the small signal analysis. In this paper, the principle and method of speed estimation and control set-up are described, as well as the results of an experiment that verified the effectiveness of the proposed method.

An improved dynamic hysteresis model is proposed for the prediction of hysteresis loop of electrical steel up to mean frequencies, taking into account the skin effect. In previous works, the analytical solution of the diffusion equation for low frequency (DELF) was coupled with the inverse static Jiles-Atherton (JA) model in order to represent the hysteresis behavior for a lamination. In the present paper, this approach is improved to ensure the reproducibility of measured hysteresis loops at mean frequency. The results of simulation are compared with the experimental ones. The selected results for frequencies 50 Hz, 100 Hz, 200 Hz and 400 Hz are presented and discussed.

The objective of this paper is to develop a novel combined MPPT-pitch angle robust control system of a variable-speed wind turbine. The direct driven wind turbine using the permanent magnet synchronous generator (PMSG) is connected to the grid by means of fully controlled frequency converters, which consist of a pulse width-modulation PWM rectifier connected to an inverter via an intermediate DC bus. In order to maximize the exploited wind power and benefit from a wide range of the wind speed, a novel combined maximum power point tracking (MPPT)-Pitch angle control is developed using only one low cost circuit based on Neural Network (ANN), which allows the PMSG to operate at an optimal speed to extract maximum power when this last is lower than nominal power, and limit the extra power. To achieve feeding the grid with high-power and good quality of electrical energy, the inverter is controlled by (PWM) in a way to deliver only the active power into the grid, and thus to obtain a unit power factor. DC-link voltage is also controlled by the inverter. The dynamic and steady-state performances of the wind energy conversion system (WECS) are carried by using Matlab Simulink.