Robust predictive sensorless control method for doubly fed induction generator controlled by matrix converter

Abstract

Matrix converter has many advantages such as no need for high value filters, capability of bidirectional energy conversion and no need for DC bus. Nevertheless, modulation methods are very difficult and require high computational effort. Therefore, model predictive control method has been implemented to simplify the complexity of this topology. However, the weakness of this control method is using mathematical model and affected by parameter changes due to the external disturbances, frequency and temperature variations. To eliminate the variations of rotor resistance and stator resistance, a reduced-order extended Kalman filter has been implemented as an observer to estimate them resulting from frequency and temperature variations, sensor noise or uncertainties. The algorithm is performed using TMS320F28335 digital signal processor that is a 32-bit floating-point processor operating at 150 MHz. Results obtained from experimental setup are compared with the previous studies and the performance of the proposed method is confirmed. K E Y W O R D S doubly fed induction generator, matrix converter, model predictive control, observer, power quality, reduced-order extended Kalman filter 1 | INTRODUCTION Matrix topology is a converter that can provide AC to AC power conversion and consists of bidirectional nine switches that are connected to each other. The output voltage is generated directly from the input voltage due to does not have large energy storage elements such as inductor or capacitor between input and output. Also, due to the switches are bidirectional,

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

“…By replacing Equations ( 18) and (22) in Equation ( 24), the wind turbine torque components can be expressed as Equations ( 26) and (27). Figure 4 shows that at maximum power points at different wind speeds, the Equation ( 23) exists.…”

“…By replacing Equations ( 18) and (22) in Equation ( 24), the wind turbine torque components can be expressed as Equations ( 26) and (27). )…”

“…Bagheri [26] introduces a new independent approach of external factors for eccentricity fault detection and discrimination in induction motors and estimates the exact severities of the faulty components using an unscented Kalman-Bucy filter (UKBF), which shows efficient performance in different types of eccentricity faults. Ortatepe and Karaarslan [27] have proposed the employment of a reduced-order EKF instead of the full-order EKF in a DFIG-based wind turbine to increase the system stability against variations of rotor and stator resistors. In this paper, a model predictive control method is used to provide easy implementation of the matrix converter, and in order to eliminate disadvantages of the model predictive control method, a reduced-order EKF has been applied as an observer to the reducing of the execution time of the algorithm.…”

Power Control of a Grid-Connected Doubly Fed Induction Generator Using H∞ Control and Kalman Filter

“…Strategy can provide the desired performance under various uncertainties in operating conditions. A control approach for DFIG has been performed by the combination of Matrix Converter with reduced-order EKF and Model Predictive Control method [22]. Although using reduced-order EKF has eliminated the drawback of the Model Predictive Control method in perturbations, the inductance changes have not been discussed.…”

“…e MH-DRPC strategy achieve sinusoidal forms of generated AC current with a constant frequency with minimal current total harmonic distortion and minimum output voltage ripple irrespective of the wind speed fluctuations and the robustness against parametric variations, but it has not been investigated about the measurement uncertainties. In contrast to [18][19][20][21][22][23][24][25][26] improving the performance of DFIG by using the H ∞ controller and other controllers, this paper intends to improve both the performance and the stability of the DFIG in the presence of both model and measurement uncertainties by proposing a new controller based on the combination of H ∞ controller method with the Kalman filter compared to the classical vector control. Table 1 shows the advantages and limitations of other control methods and the proposed method.…”