Showing 5 results for Induction Machine
F. Bagheri, H. Khaloozadeh, K. Abbaszadeh,
Volume 3, Issue 3 (7-2007)
Abstract
This paper presents a parametric low differential order model, suitable for
mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman
filter is proposed for recursively estimating the states and parameters of continuous–time
model with discrete measurements for fault detection ends. Typical motor faults as interturn
short circuit and increased winding resistance are taken into account. The models are
validated against winding function induction motor modeling which is well known in
machine modeling field. The validation shows very good agreement between proposed
method simulations and winding function method, for short-turn stator fault detection.
M. M. Rezaei, M. Mirsalim,
Volume 6, Issue 2 (6-2010)
Abstract
Here, a new fuzzy direct torque control algorithm for induction motors is
proposed. As in the classical direct torque control, the inverter gate control signals directly
come from the optimum switching voltage vector look-up table, the best voltage space
vector selection is a key factor to obtain minimum torque and flux ripples. In the proposed
approach, the best voltage space vector is selected using a new fuzzy method. A simulation
model is built up and the torque and flux ripples of basic direct torque control and the
proposed method are compared. The simulation results show that the torque and flux
ripples are significantly decreased and in addition, the switching frequency can be fixed.
H. Yaghobi,
Volume 13, Issue 1 (3-2017)
Abstract
Condition monitoring and protection methods based on the analysis of the machine's current are widely used according to non-invasive characteristics of current transformers. It should be noted that, these sensors are installed by default in the machine control center. On the other hand, condition monitoring based on mathematical methods has been proposed in literature. However, they are model based and are too complex. Artificial neural network (ANN) methods are robust and less model dependent for fault diagnosis when the fault signature can be directly achieved using the sampling data. In this procedure, the state of internal process will be ignored. Therefore, generalized regression neural network (GRNN) based method is presented in this paper that uses negative sequence currents (calculated from the machine's currents) as inputs to detect and locate an inter-turn fault in the stator windings of the induction motor. Turn-to-turn fault by changing the contact resistance and various numbers of shorted turns for realizing the fault severity has been modeled by Matlab/Simulink. The simulation and experimental results show that the proposed method is effective for the diagnosis of stator inter-turn fault in induction motor under the supply voltage unbalances.
M. H. Lazreg, A. Bentaallah,
Volume 15, Issue 1 (3-2019)
Abstract
This article presents a sensorless five level DTC control based on neural networks using Extended Kalman Filter (EKF) applied to Double Star Induction Machine (DSIM). The application of the DTC control brings a very interesting solution to the problems of robustness and dynamics. However, this control has some drawbacks such as the uncontrolled of the switching frequency and the strong ripple torque. To improve the performance of the system to be controlled, robust techniques have been applied, namely artificial neural networks. In order to reduce the number of sensors used, and thus the cost of installation, Extended Kalman filter is used to estimate the rotor speed. By viewing the simulation results using the MATLAB language for the control. The results of simulations obtained showed a very satisfactory behaviour of the machine.
M. Ehsani, A. Oraee, B. Abdi, V. Behnamgol, S. M. Hakimi,
Volume 19, Issue 1 (3-2023)
Abstract
A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear dynamics and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, dynamic sliding mode method is used. Uncertainties bound is not required in the suggested algorithm, since the adaptive gain in the controller relation is used in this study. Convergence of the sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control algorithm is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.
