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Showing 4 results for Sensorless Control

Saba Sedghizadeh , Caro Lucas , Hassan Ghafoori Fard ,
Volume 5, Issue 2 (6-2009)

An adaptive online flux-linkage estimation method for the sensorless control of switched reluctance motor (SRM) drive is presented in this paper. Sensorless operation is achieved through a binary observer based algorithm. In order to avoid using the look up tables of motor characteristics, which makes the system, depends on motor parameters, an adaptive identification algorithm is used to estimate of the nonlinear flux-linkage parameters. This method makes position and speed estimation more accurate and robust towards any model uncertainty, also it is suitable replacement for a priori knowledge of motor characteristics.
R. Pour Ebrahim, S. Tohidi, A. Younesi,
Volume 14, Issue 1 (3-2018)

In this paper, a new sensorless model reference adaptive method is used for direct control of active and reactive power of the doubly fed induction generator (DFIG). In order to estimate the rotor speed, a high frequency signal injection scheme is implemented. In this study, to improve the accuracy of speed estimation, two methods are suggested. First, the coefficients of proportional-integral (PI) blocks are optimized by using Krill Herd algorithm. In the second method, the fuzzy logic control method is applied in the estimator structure instead of PI controllers. The simulation results for the proposed methods illustrate that the estimated speed perfectly matches the actual speed of the DFIG. In addition, the desired slip value is achieved due to the accurate response. On the other hand, the active and reactive power responses have fast dynamics and relatively low oscillations. Moreover, the fuzzy controller shows more robustness against the variations of machine parameters.

M. H. Lazreg, A. Bentaallah,
Volume 15, Issue 1 (3-2019)

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.

B. Yassine, Z. Fatiha, L. Chrifi-Alaoui,
Volume 16, Issue 1 (3-2020)

This paper suggests novel sensorless speed estimation for an induction motor (IM) based on a stator current model reference adaptive system (IS-MRAS) scheme. The IS-MRAS scheme uses the error between the reference and estimated stator current vectors and the rotor speed. Observing rotor flux and the speed estimating using the conventional MRAS technique is confronted with certain problems related to the presence of the pure integrator and the rotor resistance causing offsets at low speeds, as proved by the most recent publications. These offsets are disastrous in sensorless control since these signals are no longer suitable to calculate of park angle (θs). This paper discusses the new MRAS approach (IS-MRAS) for on-line identification of the rotor resistance suitable for compensating offsets and solving problems of ordinary MRAS at low speed. This new MRAS approach used to estimate the components of the rotor flux and rotor speed without using the voltage model with on-line Setting parameters (Kp, K1) based on Type-2 fuzzy Logic. The results of the simulation and the experimental results are presented and show the effectiveness of the proposed technique.

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