On-line Rotor Resistance Estimation Research Articles

Model Reference Adaptive System for the Online Rotor Resistance Estimation in the Slip-Ring Machine Based Test-bench

Motor control algorithms with high dynamics are generally based on two basic approach field oriented control (FOC) and direct torque control (DTC). The idea of the first one is to decompose the stator current based on the rotor flux, the second one controls the torque based on the stator flux. Therefore, the FOC is very sensitive to the parameter accuracy regarding the drive performance. That is why it is crucial to verify the parameter identification in the real environment. On the other hand, the parameter sensitivity of DTC is much smaller since the stator flux estimation requires only the knowledge of the stator resistance. The article focuses on the verification of rotor resistance identification in the FOC based drive system by means of the slip ring machine based test bench. The recommended procedure calculates the torque based on the stator current and flux to implement model reference adaptive system for online rotor resistance estimation without signal injection.

Speed and Rotor Resistance Estimation of AC Induction Motor

The speed estimation method based on Extended Kalman Filter (EKF) is widely used in speed sensorless induction motor. The method has high accuracy and good robustness. However, EKF is sensitive to parameters. In particular, the rotor resistance becomes up to twice the original when the motor is running. This greatly affects the speed identification accuracy. This paper achieves online estimation of rotor resistance utilizing the Model Reference Adaptive Controller (MRAC) based on reactive power. This ensures that the speed is estimated with high accuracy even if the rotor resistance changes suddenly. Simulation results have been presented to verify the effectiveness of the method.

Investigation and analysis of high performance green energy induction motor drive with intelligent estimator

This paper attempts to enhance the performance of a green energy induction motor drive. The electronic power converters become indispensable part of the renewable energy systems (RES). The solar photovoltaic (PV) system is efficiently operated with artificial neural network (ANN) based maximum power point tracking (MPPT) algorithm. The inverter topologies for the green drive scheme are analyzed. To improve the drive performance a reduced switch multilevel inverter (RSMLI) is employed. As indirect field oriented control (IFOC) is used, the drive demands on-line estimation of rotor resistance. A neural learning model reference adaptive scheme (NL-MRAS) based rotor resistance estimator is found to exhibit good dynamic performance. This work also investigates the performance of the green drive with an intelligent estimator. The performance enhancement of the green energy drive obtained by ANN based MPPT for the PV system, a reduced switch MLI and an intelligent estimator is presented.

A Novel Rotor Resistance Estimation Technique for Vector Controlled Induction Motor Drive Using TS Fuzzy

Induction motor with indirect field oriented control is well suited for high performance applications due to its excellent dynamic behavior. But, the indirect field oriented controller is sensitive to variations in rotor time constant, especially variation in rotor resistance. In this study a scheme based on the Rotor flux Model Reference Adaptive Controller is used for on line identification of the rotor resistance and thus improving the steady state performance of the drive. The overriding feature of this estimation technique is the accurate identification of rotor resistance especially during transient and steady state conditions when the drive is operating at full load and at zero motor speed condition. Moroever, the effectiveness of the TS fuzzy controller utilizing rotor flux for online estimation of rotor resistance for four quadrant operation of motor drive is investigated and compared with the conventional PI and Mamdani fuzzy controller Simulation results in MATLAB/Simulink environment.have been presented to confirm the effectiviness of the technique.

A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction motor drives

Online estimation of rotor resistance is essential for high performance vector controlled drives. In this paper, a novel modified neural algorithm has been identified for the online estimation of rotor resistance. Neural based estimators are now receiving active consideration as they have a number of advantages over conventional techniques. The training algorithm of the neural network determines its learning speed, stability, weight convergence, accuracy of estimation, speed of tracking and ease of implementation. In this paper, the neural estimator has been studied with conventional and proposed learning algorithms. The sensitivity of the rotor resistance change has been tested for a wide range of variation from −50% to +50% on the stability of the drive system with and without estimator. It is quiet appealing to settle with optimal estimation time and error for the viable realization. The study is conducted extensively for estimation and tracking. The proposed learning algorithm is found to exhibit good estimation and tracking capabilities. Besides, it reduces computational complexity and, hence, more feasible for practical digital implementation.

An improved rotor resistance estimator for induction motors adaptive control

AbstractAn online rotor resistance estimator for induction motor adaptive control is presented. The rotor resistance scheme uses the rotor speed, the stator current, voltage provided by the controller, time-derivatives of the stator current and voltage. The time-derivatives of the stator current and voltage are estimated using 2nd order sliding mode observer. The finite time convergence of the estimated rotor resistance to the nominal value is achieved when the classical persistent excitation condition is satisfied by the input signal. The method is suitable for online operation to continuously update the rotor resistance value. Experimental results with online variation of the rotor resistance show that the proposed algorithm gives better performance compared to the previous work. The other interesting feature of the proposed method is the robustness properties with respect to the variation of the stator resistance both at relatively high speed and low speed.

An Improved Rotor Resistance Estimator for Induction Motors

In this paper, an online rotor resistance estimator for induction motor adaptive control is presented. The proposed algorithm is an improvement of the previous work Kenne et al.. The rotor resistance scheme uses the rotor speed, the stator current, voltage provided by the controller, time-derivatives of the stator current and voltage. The time derivatives of the stator current and voltage are estimated by using 2nd order sliding mode observer. The convergence of the estimated rotor resistance to the nominal value in finite time is achieved when the classical persistent excitation condition is satisfied by the input signal. Experimental results with 100% variation of the stator resistance and online variation of the rotor resistance show that the proposed algorithm gives better performance compared to the previous work.

Model Reference Adaptive Controller-Based Rotor Resistance and Speed Estimation Techniques for Vector Controlled Induction Motor Drive Utilizing Reactive Power

In this paper, a detailed study on the model reference adaptive controller (MRAC) utilizing the reactive power is presented for the online estimation of rotor resistance to maintain proper flux orientation in an indirect vector controlled induction motor drive. Selection of reactive power as the functional candidate in the MRAC automatically makes the system immune to the variation of stator resistance. Moreover, the unique formation of the MRAC with the instantaneous and steady-state reactive power completely eliminates the requirement of any flux estimation in the process of computation. Thus, the method is less sensitive to integrator-related problems like drift and saturation (requiring no integration). This also makes the estimation at or near zero speed quite accurate. Adding flux estimators to the MRAC, a speed sensorless scheme is developed. Simulation and experimental results have been presented to confirm the effectiveness of the technique.

2001 Middle-East Power Systems Conference 29-31 December 2001 Helwan, Cairo, Egypt

This letter presents a method for induction motor on-line rotor resistance estimation. This is important in vector control, if high-performance torque control is needed. For this purpose, a fuzzy logic technique is used to estimate the rotor resistance. The fuzzy estimator (FE) principle design is then presented, and simulations demonstrate the effectiveness of the proposed method.

Sensorless very low-speed and zero-speed estimations with online rotor resistance estimation of induction motor without signal injection

In the speed-sensorless control of the induction motor, the machine parameters (especially rotor resistance R/sub 2/) have a strong influence on the speed estimation. It is known that the simultaneous estimation of speed and R/sub 2/ is impossible in the slip-frequency-type vector control, because the rotor flux is constant. In addition, the output voltage error due to the deadtime and the voltage drop of the inverter influences the rotor flux estimation, but the simultaneous compensation of the output voltage and stator resistance R/sub 1/ is very difficult. In this paper, the R/sub 2/ estimation without adding any additional signal to the stator current is proposed. In the high-frequency range, R/sub 2/ is estimated in the transient state and, in the very low-frequency range, R/sub 2/ is estimated in proportion to the estimated R/sub 1/ when the ideal voltage source by the power op-amp is used instead of the voltage-source inverter to avoid the output voltage error. Using these algorithms, the low-speed estimation was possible and the zero-speed control was achieved under the 10%-40% load condition. This paper clarifies that the zero-speed estimation is possible with the stator and rotor resistance estimations when the output voltage is ideal.

On-line estimation of rotor resistance of an induction motor using recurrent neural networks

The authors have developed a new approach which provides an on-line estimation of the rotor resistance of an induction motor operating under vector control. Simulation results show that the new approach is capable of fast estimation of the rotor resistance with a negligible error.

Full-order estimator for induction motor states and parameters

In direct vector control of induction machines, the instantaneous rotor flux vector is measured using sensors, estimators or a combination of both. Since the basic Kalman filter is a state estimator, its use in vector-controlled schemes has received much attention, including reduced-order variants. The paper describes the application of the Kalman filter to rotor current estimators for the direct scheme. The method used is termed full order, and is advocated as an alternative to reduced-order schemes which have been developed to aid real-time implementation. A novel approximate full-order form using steady-state gain values is shown to give encouraging results while yielding a useful computational saving. Also described is the application of the full extended Kalman filter algorithm to the online estimation of rotor resistance in an induction motor drive, required for the slip-calculation algorithm of indirect vector control. Temperature variations in rotor resistance can be tracked as they occur. These performance advantages are illustrated by experimental results from a typical open-loop-controlled drive.

Observers for induction motor state and parameter estimation

The Kalman filter in its basic form is a state estimator and can be applied to the problem of estimating induction motor rotor currents in a vector control scheme. This filter is shown to combine information from the plant model with output measurements to produce an optimal estimate of the unmeasured states. Also described is the application of the extended Kalman filter algorithm to the online estimation of rotor resistance in an induction motor drive. Significant savings in computing requirements are obtained with a reduced-order model of the motor, in which measured, rather than computed, values of stator currents are used.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>