Implementation of a new flux rotor based on model reference adaptive system for sensorless direct torque control modified for induction motor

Introduction. In order to realize an efficient speed control of induction motor, speed sensors, such as encoder, resolver or tachometer may be utilized. However, some problems appear such as, need of shaft extension, which decreases the mechanical robustness of the drive, reduce the reliability, and increase in cost. Purpose. In order to eliminate of speed sensors without losing. Several solutions to solve this problem have been suggested. Based on the motor fundamental excitation model, high frequency signal injection methods. The necessity of external hardware for signal injection and the adverse influence of injecting signal on the motor performance do not constitute an advantage for this technique. Fundamental model-based strategies method using instantaneous values of stator voltages and currents to estimate the rotor speed has been investigate. Several other methods have been proposed, such as model reference adaptive system, sliding mode observers, Luenberger observer and Kalman filter. The novelty of the proposed work consists in presenting a model reference adaptive system based speed estimator for sensorless direct torque control modified for induction motor drive. The model reference adaptive system is formed with flux rotor and the estimated stator current vector. Methods. 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. Practical value. The merits of the proposed estimator are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions. The experimental results show the efficiency of the proposed speed estimation technique. Experimental results show the effectiveness of the proposed speed estimation method at nominal speed regions and speed reversal, and good results with respect to measurement speed estimation errors obtained.Вступ. Щоб реалізувати ефективне керування швидкістю асинхронного двигуна, можна використовувати датчики швидкості, такі як енкодер, резольвер або тахометр. Однак виникають деякі проблеми, такі як необхідність подовження валу, що знижує механічну міцність приводу, знижує надійність та збільшує вартість. Мета. Для усунення датчиків швидкості без втрати. Було запропоновано кілька рішень на вирішення цієї проблеми. На основі моделі основного порушення двигуна використовуються методи подачі високочастотного сигналу. Необхідність зовнішнього обладнання для подачі сигналу та несприятливий вплив подачі сигналу на роботу двигуна не є перевагою цього методу. Досліджено метод стратегій на основі фундаментальних моделей з використанням миттєвих значень напруг та струмів статора для оцінки швидкості обертання ротора. Було запропоновано кілька інших методів, таких як еталонна адаптивна система моделі, спостерігачі режиму ковзання, спостерігач Люенбергера і фільтр Калмана. Новизна запропонованої роботи полягає у поданні модельної еталонної адаптивної системи оцінки швидкості прямого бездатчикового управління моментом, модифікованої для асинхронного електроприводу. Еталонна адаптивна система моделі формується з магнітним потоком ротора та оціненим вектором струму статора. Методи. Еталонна модель використовує вимірюваний вектор струму. З іншого боку, модель, що регулюється, використовує передбачуваний вектор струму статора. Струм оцінюється шляхом вирішення рівнянь стану машини. Практична цінність. Переваги запропонованого оцінювача продемонстровані експериментально на тестовій установці, реалізованій на платі dSPACE DS1104 у різних умовах експлуатації. Експериментальні результати свідчать про ефективність запропонованої методики оцінки швидкості. Експериментальні результати показують ефективність запропонованого методу оцінки швидкості в областях номінальних швидкостей та реверсивних швидкостей, а також хороші результати щодо отриманих похибок оцінки швидкості вимірювання

Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

Field Oriented Controlled Speed Sensorless Control of Induction Motors

In this paper, a special class of adaptive control system, model reference adaptive controller (MRAC) for the speed estimation of the field oriented controlled (FOC) induction motor drive is presented. The proposed MRAC is formed using instantaneous and steady-state values of tuning signal insynchronously rotating reference frame, which is a fictitious quantity and has no physical significance. Requirement of no additional sensors makes the drive suitable for retrofit applications. The proposed MRAC-based speed sensorless field oriented controlled induction motor drive estimation technique has been simulated in MATLAB/SIMULIN

New Model Reference Adaptive System Speed Observer for Field-Oriented Control Induction Motor Drives Using Neural Networks

One of the primary advantages of field-oriented controlled induction motor for high performance application is the capability for easy field weakening and the full utilization of voltage and current rating of the inverter to obtain a wide dynamic speed rangeThis paper describes a Model Reference Adaptive System (MRAS) based scheme using Artificial Neural Network (ANN) for online speed estimation of sensorless vector controlled induction motor drive. The proposed MRAS speed observer uses the current model as an adaptive model. The neural network has been then designed and trained online by employing a back propagation network (BPN) algorithm. The estimator was designed and simulated in Matlab/Simulink. Simulation result shows a good performance of speed estimator. The simulation results show good performance in various operating conditions. Also Performance analysis of speed estimator with the change in resistances of stator is presented. Simulation results show this estimator robust to parameter variations especially resistances of stator

MRAS Based Speed Identification for Sensorless Field Oriented Controlled Induction Motor with online Identification of Stator Resistance

This paper presents a new online method of estimating the stator resistance of an induction motor simultaneously with the motor rotor speed for effective implementation of rotor field oriented control technique. Knowledge of stator resistance is required for the correct operation of speed sensorless control of the induction motor in low speed region. Since stator resistance varies with drive operating conditions, stable operation in low speed requires an appropriate on-line estimator for the stator resistance. The paper proposes the stator resistance and rotor speed estimation algorithm based on rotor flux based MRAS in a systematic manner. It enables the correct speed estimation and stable drive operation at low speed. The proposed parallel speed with stator resistance estimator is verified by MATLAB/SIMULINK simulation. A simulation result shows the robustness and accuracy of the proposed method and good speed tracking capability and fast responses have been achieved

Model predictive MRAS estimator for sensorless induction motor drives

Ph. D. ThesisThe project presents a novel model predictive reference adaptive system (MRAS) speed observer for sensorless induction motor drives applications. The proposed observer is based on the finite control set-model predictive control principle. The rotor position is calculated using a search-based optimization algorithm which ensures a minimum speed tuning error signal at each sampling period. This eliminates the need for a proportional integral (PI) controller which is conventionally employed in the adaption mechanism of MRAS observers. Extensive simulation and experimental tests have been carried out to evaluate the performance of the proposed observer. Both the simulation and the experimental results show improved performance of the MRAS scheme in both open and closed-loop sensorless modes of operation at low speeds and with different loading conditions including regeneration. The proposed scheme also improves the system robustness against motor parameter variations and increases the maximum bandwidth of the speed loop controller. However, some of the experimental results show oscillations in the estimated rotor speed, especially at light loading conditions. Furthermore, due to the use of the voltage equation in the reference model, the scheme remains sensitive, to a certain extent, to the variations in the machine parameters. Therefore, to reduce rotor speed oscillations at light loading conditions, an adaptive filter is employed in the speed extraction mechanism, where an adaptation mechanism is proposed to adapt the filter time constant depending on the dynamic state of the system. Furthermore, a voltage compensating method is employed in the reference model of the MP-MRAS observer to address the problems associated with sensitivity to motor parameter variation. The performance of the proposed scheme is evaluated both experimentally and by simulation. Results confirm the effectiveness of the proposed scheme for sensorless speed control of IM drives

Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space

Model Reference Adaptive System (MRAS) Based Speed Sensorless Vector Control of Induction Motor Drive

No Abstrac