Indirect Vector Controlled Induction Motor Research Articles

Adaptive fuzzy self-learning controller based rotor resistance estimator for vector controlled induction motor drive

This paper presents an intelligent approach to identify and adapt the rotor resistance for an indirect vector controlled induction motor drive. This command is affected by rotor resistance; the variation of this parameter could distort the decoupling between flux and torque and, consequently, lead to deterioration of drive performance. To overcome this problem, a fuzzy estimator is provided to identify the real value of rotor resistance in order to obtain a vector control optimal. Then we propose a fuzzy adaptive control strategy fits into the learning methods context by modifying the consequences of fuzzy estimator. Regarding the learning algorithm, our solution envisages the use of a fuzzy inverse model, combined with a mechanism that acts based on estimator rules by modifying the consequents according to a certain criterion, so as to increase the system robustness, and avoid unnecessary oscillation in the control signal. The suggested rotor resistance identification approach has been validated by simulation study.

Indirect Vector Controlled Induction Motor Drive with PI Controller & Current Controlled VSI

Indirect Vector Controlled Induction Motor Drive with PI Controller & Current Controlled VSI

Performance improvement of indirect vector controlled induction motor drive using multi-level inverter

Multi-level inverter has emerged as an important topology in the area of high power-medium voltage control applications due to its several advantages such as a better output voltage with reduced total harmonic distortion (THD), reduction of voltage ratings of the power semiconductor switching devices and also the reduced electro-magnetic interference problems etc. This paper presents the performance of an indirect vector controlled induction motor drive fed from five-level, three-level and two-level inverters under different operating conditions (dynamic and steady state). The control scheme uses space vector modulation (SVM) technique for five-level, three-level and two-level inverter circuits. For two-level inverter control, an SVM algorithm in which the calculation of switching times proportional to the instantaneous values of the reference phase voltage. It eliminates the calculation of sector and angle information. For three-level and five-level inverter control, a simplified SVM technique is implemented, which reduces the coordinate transformations complications in the algorithms. In this, the space vector diagram of the three-level and five-level inverter is decomposed into six space vector diagrams of two-level inverters. The torque ripple and the performance of induction motor drive with three-level, five-level and two-level inverters are compared under different operating conditions.

Real-time loss minimization control in induction machines based on DSP TMS320LF2812

This paper presents a DSP based implementation of simple and very useful control algorithm for the real-time efficiency optimization of the indirect vectorcontrolled induction motor drives. Conventional field-oriented induction motor drives operate at rated flux even at low load. To improve the efficiency of the existing induction motors, it is important to regulate the magnetization flux of the motor in the desired operating range. This paper presents techniques for minimizing power loss (the copper and core losses) of induction motor based on determination of an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. Thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The algorithm offers a fast convergence. The complete closed loop vector control of the proposed LMC-based IM drive is successfully implemented in real-time using digital signal processor DSP TMS320LF2812 for 1HP motor induction motor. The close agreement between the simulation by Matlab/Simulink and the experimental results confirms the validity and usefulness of the proposed techniques. The proposed LMC in a comparison with conventional FOC can reduce total losses from 5% to 67.2% for all load ranges.

Neural learning adaptive system using simplified reactive power reference model based speed estimation in sensorless indirect vector controlled induction motor drives

Abstract This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

Artificial Neural Network Based Controller for Speed Control of An Induction Motor (IM) using Indirect Vector Control Method

In this paper, an implementation of intelligent controller for speed control of an induction motor (IM) using indirect vector control method has been developed and analyzed in detail. The project is complete mathematical model of field orientation control (FOC) induction motor is described and simulated in MATLAB for studies a 50 HP(37KW), cage type induction motor has been considered .The comparative performance of PI, Fuzzy and Neural network control techniques have been presented and analyzed in this work. The present approach avoids the use of flux and speed sensor which increase the installation cost and mechanical robustness .The neural network based controller is found to be a very useful technique to obtain a high performance speed control. The scheme consist of neural network controller, reference modal, an algorithm for changing the neural network weight in order that speed of the derive can track performance speed. The indirect vector controlled induction motor drive involve decoupling of the stator current in to torque and flux producing components. DOI: http://dx.doi.org/10.11591/ijpeds.v2i4.567

Fuzzy Logic Speed Controller of 3-Phase Induction Motors for Efficiency Improvement

The paper presents an accurate loss model based controller of an induction motor to calculate the optimal air gap flux. The model includes copper losses, iron losses, harmonic losses, friction and windage losses, and stray losses. These losses are represented as a function of the air gap flux. By using the calculated optimal air gap flux compared with rated flux for speed sensorless indirect vector controlled induction motor, an improvement in motor efficiency is achieved. The motor speed performance is improved using a fuzzy logic speed controller instead of a PI controller. The fuzzy logic speed controller was simulated using the fuzzy control interface block of MATLAB/SIMULINK program. The control algorithm is experimentally tested within a PC under RTAI-Linux. The simulation and experimental results show the improvement in motor efficiency and speed performance.

Neuro-fuzzy-based torque ripple reduction and performance improvement of VSI fed induction motor drive

An indirect vector control is used in various industrial induction motor drives for better dynamic performance. But the performance of closed-loop controlled induction motor drive is largely influenced by the type of current controllers used. Usually, proportional-integral (PI)-based current controllers are used due its simplicity. But these PI controllers suffer from tuning problem. To overcome the problem, this paper presents adaptive neuro-fuzzy (ANFIS)-based current controllers for indirect vector controlled VSI fed induction motor drive in order to minimise the torque ripple. The proposed ANFIS controller significantly reduces the torque ripple compared to that of conventional PI controllers without using any filter. In this, the current command values and actual values of current are fed to the ANFIS-based controllers and the controller is trained based on the obtained values. The performance of the indirect vector controlled induction motor drive has been simulated at different operating conditions using the ANFIS controller and the obtained results are compared with PI controller. Additionally in the implemented SVM algorithm, the switching times proportional to the instantaneous values of the reference phase voltages. It eliminates the need the calculation of sector and angle information unlike conventional SVM method.

High Performance Speed Sensorless Control of Three-Phase Induction Motor Based on Cloud Computing

Induction motor is a cast of alternating current motor where charge endures allotted to the rotor close-at-hand deputation of conductive charge. These motors are broadly applied in industrial claim due to they are arduous along with adhere no contacts. The speed controller of deltoid phase induction motor is applied to alleviate the aberration of speed. The central constructivist of this paper is to accrue the performance of speed sensorless control of three phase induction motor. To increase its performance, this paper presents a modified method for speed controller of an indirect vector-controlled induction motor drive using cloud computing technique. Our methodology depends on speed sensorless scheme to obtain the speed signal feedback; the speed estimator is based on model reference adaptive control that uses the stator current and rotor flux as state variables for estimating the speed. In this method, the stator current error is represented as a function of first degree of the estimated speed error. An analysis and simulation of the tried algorithm is birthed and applied easing a TMS320C31 floating-point notational alert Processor. And accumulate the action of the three phase induction motor we conceived our appraisals affixed to the accountant based on cloud computing tactics. This intelligent policy uses the guidelines of the speed controller efficiently. Simulation and experimental results depicted that the motor speed is decelerated articulately to destine its illusion apprise without above and inferior smack and with about zero steady state error. The apprised accelerate alert and its dispatching buoy amassed off line from burlesque. After effects display an advantageous affinity among the accounted speed alert and it's dispatching allocated as well as aped speed flares.

Performance Analysis of Field Oriented Induction Motor using Fuzzy PI and Fuzzy Logic based Model Reference Adaptive Control

This paper presents the dynamic performances of an indirect vector controlled induction motor (IVCIM) using a fuzzy logic (FL) based model reference adaptive control (MRAC) slip gain tuner for speed regulation in the drive. In high performance AC drives the motor speed should closely match with the specified reference speed irrespective of the variations in the load, motor parameters and model uncertainties. Two fuzzy controllers combined with MRAC reactive power and stator direct axis (d-axis) voltage estimator have been used to tune the slip gain of the IVCIM drive against parameter variations and model uncertainties. An integrated mathematical model of the control scheme has been developed and simulated in MATLAB for Indirect vector control of an Induction motor. The simulated performances of the FL-MRAC slip gain tuner based IVCIM drive is compared to fuzzy PI controller. The simulated results in different dynamic operating conditions such as sudden change in command speed, step change in load, etc are demonstrated through necessary waveforms. The comparison of simulated results show that the fuzzy logic MRAC slip gain tuner based IVCIM drive is more robust and effective in minimizing the detuning effect in the drive due to parameter variations and model uncertainties.

INFLUENCE OF CONSTANT VALUES AND MOTOR PARAMETERS DEVIATIONS ON THE PERFORMANCE OF THE ADAPTIVE SLIDINGMODE OBSERVER IN A SENSORLESS INDUCTION MOTOR DRIVE

The adaptive sliding-mode observer has been widely used to estimate the rotor ∞ux and rotor speed in inverter-fed sensorless induction motor drives. However, the technique requires setting a priori the sliding-mode observer constants and also knowledge of the induction motor parameters. This particular aspect can cause signiflcant errors in the estimation of the rotor speed used in sensorless control schemes. Changes in the induction machine parameters due to temperature or difierent saturation levels will afiect the dynamic operation of the observer despite its adaptive nature. In this context, a sensitivity study of the adaptive sliding-mode observer is presented and discussed in this paper. Various experiments are performed on a sensorless indirect vector-controlled induction motor drive under a variety of conditions to verify the observer robustness.

ROTOR RESISTANCE IDENTIFICATION FOR SPEED SENSORLESS VECTOR CONTROLLED INDUCTION MOTOR DRIVES TAKING SATURATION INTO ACCOUNT

This paper aims at developing a method of rotor resistance estimation for speed sensorless indirect vector controlled induction motor drive taking the effect of magnetic flux saturation into account. A mathematical dynamic model of an induction motor as influenced by magnetic circuit saturation is presented. Moreover, a modified structure of indirect vector controller scheme is proposed which involves the saturated value of the magnetizing inductance. In this scheme, an effective online method for rotor resistance estimation is based on a modified model reference adaptive system (MRAS) to achieve high-precise control in a wide range of motor speed. The motor speed is estimated from the difference between the estimated synchronous speed and slip speed. The online magnetizing inductance estimation algorithm to operate within the rotor resistance estimation is presented. Digital simulations have been carried out in order to evaluate the effectiveness of the proposed sensorless drive system. The results have proven excellent steady-state and dynamic performances of the drive system, which confirms validity of the proposed scheme.

Performance of a reactive power-based adaptive estimation of inverse rotor time constant for vector controlled induction motor drives

A detail investigation on an adaptive estimation of rotor time constant is presented here. The adaptation mechanism forms a Model Reference Adaptive System (MRAS) for the online estimation of inverse rotor time constant for high performance Indirect Vector Controlled Induction Motor Drive. The instantaneous and steady state reactive powers are utilised in the proposed MRAS. A merit of the scheme is that the flux computation is not required in both the models (reference model and adjustable model) of MRAS. So, the low speed estimation problems like drift and saturation of integrator (related to flux estimation from voltage model) are overcome. The use of reactive power as a functional candidate of MRAS naturally makes the scheme independent of stator resistance. The workability of the proposed MRAS is verified through extensive simulation and experiment. The stability of the system is investigated and a study on the sensitivity to stator and rotor parameters is reported. The technique is simple and can be implemented in the existing indirect vector controlled system without any additional hardware.

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.

Simultaneous Estimation of Speed and Rotor Resistance in Sensorless Induction Motor Vector Controlled Drive

In this paper a new sensorless indirect vector controlled induction motor drive robust against rotor resistance variation is presented. The speed and rotor resistance are estimated simultaneously, which is reported in many papers as impossible. The estimation is achieved using a reduced order Kalman filter to reduce the computational burden. This algorithm uses a reduced order model of the motor. The model takes into account the coupling between the electrical and mechanical modes, which is true for small size machines. The method proposed in this paper is applicable to a large category of induction motor drives with a gradually varying load torque such as viscous friction, fan/blower and centrifugal pump. A fully real-time digital simulation, a new powerful tool for rapid control prototyping, is carried out to verify the effectiveness of the proposed method. Results show that accurate estimation is achieved under both transient and steady state conditions without injecting any external signal. This achievement is, to the best of authors' knowledge, reported for the first time and is believed to be of great importance for induction machine sensorless control.

Stator and Rotor Resistance Observers for Induction Motor Drive Using Fuzzy Logic and Artificial Neural Networks

This paper presents a new observer for the rotor resistance of an indirect vector controlled induction motor drive using artificial neural networks supplemented by a fuzzy logic based stator resistance observer. The error between the rotor flux linkages based on a neural network model and a voltage model is back propagated to adjust the weights of the neural network model for the rotor resistance estimation. The error between the measured stator current and its corresponding estimated value is mapped to a change in stator resistance with a proposed fuzzy logic. The stator resistance observed with this approach is used to correct the rotor resistance observer using neural networks. The performance of these observers and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations. Both modeling and experimental data on tracking performances of these observers are presented. With this approach accurate rotor resistance estimation was achieved and was made insensitive to stator resistance variations both in modeling and experiment.

Induction motor servo drive using robust PID-like neuro-fuzzy controller

A robust PID-like neuro-fuzzy controller, which has an ability to compensate for parameter variation, is proposed and applied to the speed control of the indirect vector-controlled induction motor. The controller gains are adjusted on-line using the tuning algorithm based on an artificial neural network (ANN). And a variable learning rate algorithm is proposed to improve the tracking performance while keeping the robustness. Simulation and experimental results confirm that good dynamic performance and high robustness to parameter variation and disturbance can be achieved by means of the proposed controller.

Fast efficiency optimization techniques for the indirect vector-controlled induction motor drives

This paper presents two simple and very useful techniques for the efficiency optimization of the indirect vector-controlled induction motor drives. In the synchronously rotating reference frame the flux-producing current is controlled until the power at the DC link is minimum. Of the two techniques, the first method controls the flux-producing current in a regular and smooth manner. The second technique combines loss model and search approaches in a unique way to propose a hybrid method, where the first estimate is from the loss model approach and the subsequent adjustment of the flux is through the search technique. Both the algorithms are simple, easily realizable, and offer fast convergence. Also, smooth control of the flux offers excellent dynamic performance. A comparative assessment shows that the hybrid method is the best, even if an approximate equivalent circuit for the induction motor is used for the analysis and optimization of the losses. The close agreement between the simulation and the experimental results confirms the validity and usefulness of the proposed techniques.

Efficiency Optimization of a Solar Boat IM Drive Employing Variable DC Link Voltage and Fuzzy Control

This paper describes a fuzzy logic based on-line efficiency optimization control of a solar boat drive that uses an indirect vector controlled induction motor for either speed or torque control. Besides acting on the flux level to reach optimum balance between the core and copper losses, this controller also acts upon the DC link voltage, in order to reduce the inverter losses as well. Due to its quadratic characteristics, the load torque is quite small at low to medium speeds, and the potential for energy saving is great. An experimental drive system with the proposed controller implemented on a TMS320C25 DSP based control board was constructed and tested in laboratory to validate the proposed technique.

Digital Sliding Mode Position Control of an Induction Motor

A sliding mode controller is employed to improve the robust performance of the position control of an induction motor in response to mechanical parameter uncertainty or load torque variations. In this paper a discrete-time sliding mode controller is proposed for an indirect-vector controlled induction motor. Using the proposed controller, the position control of an induction motor is designed and the effects of the parameter uncertainty and the load torque variations on the performance of the controller are investigated. Simulation results reveal the effectiveness of the proposed controller.