A MRAS-based adaptive pseudoreduced-order flux observer for sensorless induction motor drives

The performance of vector-controlled sensorless induction motor drives is generally poor at very low speeds, especially at zero speed due to offset and drift components in the acquired feedback signals, and the increased sensitivity of dynamic performance to model parameter mismatch resulting especially from stator resistance variations. The speed estimation is adversely affected by stator resistance variations due to temperature and frequency changes. This is particularly significant at very low speeds where the calculated flux deviates from its set values. Therefore, it is necessary to compensate for the parameter variation in sensorless induction motor drives, particularly at very low speeds. This paper presents a novel method of estimating both the shaft speed and stator resistance of an induction motor. In this novel scheme, an adaptive pseudoreduced-order flux observer (APFO) is developed. In comparison to the adaptive full-order flux observer (AFFO), the proposed method consumes less computational time, and provides a better stator resistance estimation dynamic performance. Both simulation and experimental results confirm the superiority of the proposed APFO scheme for a wide range of resistance variations from 0 to 100%.     

基于自适应PI在线补偿感应电机定子电阻

感应电机定子电阻变化是影响直接转矩控制低速性能的主要原因之一。该文分析了定子电阻变化对直接转矩控制性能的影响，并指出可能出现系统不稳定的现象。根据实际定子电流和给定定子电流的差值，提出了基于自适应PI的在线定子电阻补偿方法，解决了定予电阻对系统性能影响的问题。仿真结果表明，该文提出的方案能实现对实际定子电阻的辨识并对控制系统所采用定子电阻在线补偿，从而改善了直接转矩控制系统的低速性能。     

Two-Level VSC Based Predictive Direct Torque Control of the Doubly Fed Induction Machine With Reduced Torque and Flux Ripples at Low Constant Switching Frequency

In this paper, a new predictive direct torque control (DTC) strategy of the doubly fed induction machine (DFIM) is presented. It is especially designed to operate at a considerably low constant switching frequency, reducing the electromagnetic torque and rotor flux ripples, in order to provide good steady-state and fast dynamic performances. This control is convenient for high power drive and generator applications, with restricted switching frequency. The DFIM is connected to the grid by the stator and the rotor is fed by a two level voltage source converter. In addition, this control method allows to implement a technique that reduces the switching power losses of the converter. Finally, experimental results show that the proposed DTC method effectively reduces the torque and flux ripples at low switching frequency, even under variable speed operation conditions.     

Design and implementation of the extended Kalman filter for thespeed and rotor position estimation of brushless DC motor

A method for speed and rotor position estimation of a brushless DC motor (BLDCM) is presented in this paper. An extended Kalman filter (EKF) is employed to estimate the motor state variables by only using measurements of the stator fine voltages and currents. When applying the EKF, it was necessary to solve some specific problems related to the voltage and current waveforms of the BLDCM. During the estimation procedure, the voltage- and current-measuring signals are not filtered, which is otherwise usually done when applying similar methods. The voltage average value during the sampling interval is obtained by combining measurements and calculations, owing to the application of the predictive current controller which is based on the mathematical model of motor. Two variants of the estimation algorithm are considered: (1) speed and rotor position are estimated with constant motor parameters and (2) the stator resistance is estimated simultaneously with motor state variables. In order to verify the estimation results, the laboratory setup has been constructed using a motor with ratings of 1.5 kW, 2000 r/min, fed by an insulated gate bipolar transistor inverter. The speed and current controls, as well as the estimation algorithm, have been implemented by a digital signal processor (TMS320C50). The experimental results show that is possible to estimate the speed and rotor position of the BLDCM with sufficient accuracy in both steady-state and dynamic operation. Introducing the estimation of the stator resistance, the speed estimation accuracy is increased, particularly at low speeds. At the end of the paper, the characteristics of the sensorless drive are analyzed. A sensorless speed control system has been achieved with maximum steady-state error between reference and actual motor speed of ±1% at speeds above 5% of the rated value     

Tuning the stator resistance of induction motors using artificialneural network

Tuning the stator resistance of induction motors is very important, especially when it is used to implement direct torque control (DTC) in which the stator resistance is a main parameter. In this paper, an artificial network (ANN) is used to accomplish tuning of the stator resistance of an induction motor. The parallel recursive prediction error and backpropagation training algorithms were used in training the neural network for the simulation and experimental results, respectively. The neural network used to tune the stator resistance was trained on-line, making the DTC strategy more robust and accurate. Simulation results are presented for three different neural-network configurations showing the efficiency of the tuning process. Experimental results were obtained for one of the three neural-network configurations. Both simulation and experimental results showed that the ANN have tuned the stator resistance in the controller to track actual resistance of the machine     

An improved flux observer based on PLL frequency estimator for sensorless vector control of induction motors

This paper presents an improved method of flux estimation for sensorless vector control of induction motors based on a phase locked loop (PLL) programmable low-pass filter (LPF) and a vector rotator. A PLL synchronized with the voltage vector is used for stator frequency estimation. The pure integration of the stator voltage equations is difficult to implement and LPFs with a fixed cutoff provide good estimates only in the relatively high frequency range-at low frequencies, the estimates fail in both magnitude and phase. The method proposed corrects the above problem for a wide range of speeds. Simulations and experimental results on a 0.25-hp three-phase induction machine verify the validity of the approach.     

A new stator resistance tuning method for stator-flux-orientedvector-controlled induction motor drive

Field-oriented-controlled induction motor drives have been widely used over the last several years. Conventional direct stator-flux-oriented control schemes have the disadvantage of poor performance in the low-speed operating area when the stator flux is calculated using the voltage model, due to the stator resistance uncertainties and variations. In this paper, a new closed-loop stator-flux estimation method for a stator-flux-oriented vector-controlled induction motor drive is presented in which the stator resistance value is updated during operation. This method is based on a simple algorithm capable of running in a low-cost microcontroller, which is derived from the dynamic model of the induction machine. The effects of stator resistance detuning, especially in the low-speed operating region, are investigated and simulation results are shown. The motor drive system as well as the control logic and the resistance estimator are simulated and characteristic simulation results are derived. In addition, the proposed control scheme is experimentally implemented and some characteristic experimental results are shown. The simulation as well as the experimental results reveal that the proposed method is able to obtain precise flux and torque control, even for very low operating frequencies     

Instability phenomena and remedies in sensorless indirect fieldoriented control

Sensorless indirect field oriented induction motor drives using information in the fundamental excitation for speed estimation are considered. It is shown that instability phenomena which are not present for sensored operation occur, both for nominal and low speeds. The problems at nominal speeds are remedied by adding a term to the standard slip relation, such that approximate “voltage model” characteristics are obtained. The low-speed problems are remedied by careful choice of the model machine parameters and the torque-producing stator current component. It is also shown how a speed estimation algorithm based on a voltage error can be designed. Its asymptotic properties are similar to those of a current error-based least squares estimator such as the extended Kalman filter. As no state observer is required, the sensorless control system is computationally very efficient, requiring only about 15 multiplications and two divisions per sample for a digital implementation     

Implementation of a direct control algorithm for induction motorsbased on discrete space vector modulation

The basic concept of direct torque control of induction machines is investigated in order to emphasize the effects produced by a given voltage vector on stator flux and torque variations. The low number of voltage vectors which can be applied to the machine using the basic DTC scheme may cause undesired torque and current ripple. An improvement of the drive performance can be obtained using a new DTC algorithm based on the application of the space vector modulation (SVM) for prefixed time intervals. In this way a sort of discrete space vector modulation (DSVM) is introduced. Numerical simulations and experimental tests have been carried out to validate the proposed method     

An improved DTC-SVM method for matrix converter drives using a deadbeat scheme

In this paper, a simple direct torque control (DTC) method for sensorless matrix converter drives is proposed, which is characterized by a simple structure, minimal torque ripple and unity input power factor. Also, a good sensorless speed-control performance in the low speed operation is obtained, while maintaining constant switching frequency and fast torque dynamics. It is possible to combine the advantages of matrix converters with the advantages of the DTC strategy using space vector modulation a deadbeat algorithm in the stator flux reference frame. Experimental results are shown to illustrate the feasibility of the proposed strategy.     

Sensorless field orientation control of induction machines based ona mutual MRAS scheme

A mutual model reference adaptive system (MRAS) is proposed to implement a position sensorless field-orientation control (FOC) of an induction machine. The reference model and adjustable model used in the mutual MRAS scheme are interchangeable. Therefore, it can be used to identify both rotor speed and the stator resistance of an induction machine. For the rotor speed estimation, one model is used as a reference model and another is the adjustable model. Pure integration and stator leakage inductance are removed from the reference model, resulting in robust performance in low and high speed ranges. For the stator resistance identification, the two models switch their roles. To further improve estimation accuracy of the rotor speed and stator resistance, a simple on-line rotor time constant identification is included. Computer simulations and experimental results are given to show its effectiveness     

A novel technique for estimation and control of stator flux of a salient-pole PMSM in DTC method based on MTPF

A permanent-magnet synchronous machine (PMSM) can be controlled using the direct torque control (DTC) technique in three different ways, i.e., by controlling flux, reactive torque and rotor d-axis current. Frequently, the DTC technique controls the speed of the motor by controlling stator flux with the aim of obtaining an optimal torque. A varying flux, proportional to the torque, may be used instead of a fixed flux, resulting in a maximum torque per ampere or maximum torque per flux (MTPF). In this paper, a reference-flux-generating method is followed to achieve the MTPF. An approximate equation is then derived using numerical techniques in order to obtain the reference flux from the torque. This equation is then applied to the DTC control system in order to obtain the reference flux. The control scheme has been verified by simulation and tests on a salient-pole permanent-magnet synchronous motor.     

An innovative direct self-control scheme for induction motor drives

The paper presents a new direct self-control (DSC) scheme for induction motor drives using the stator voltage third harmonic component in order to estimate the air-gap flux and the torque as well as to synchronize the supply voltage vector. Compared to previous DSC schemes the new one is independent from any motor parameter variation, specifically on stator resistance thus showing better performances at low speeds. The paper starts with a quick review on standard DSC main features pointing out the influence of stator resistance variations on the flux and torque control. The new DSC scheme is then introduced and evaluated by simulations and experimental tests on a 1.5 kW induction motor drive     

Hybrid Cascaded H-Bridge Multilevel-Inverter Induction-Motor-Drive Direct Torque Control for Automotive Applications

This paper presents a hybrid cascaded H-bridge multilevel motor drive direct torque control (DTC) scheme for electric vehicles (EVs) or hybrid EVs. The control method is based on DTC operating principles. The stator voltage vector reference is computed from the stator flux and torque errors imposed by the flux and torque controllers. This voltage reference is then generated using a hybrid cascaded H-bridge multilevel inverter, where each phase of the inverter can be implemented using a dc source, which would be available from fuel cells, batteries, or ultracapacitors. This inverter provides nearly sinusoidal voltages with very low distortion, even without filtering, using fewer switching devices. In addition, the multilevel inverter can generate a high and fixed switching frequency output voltage with fewer switching losses, since only the small power cells of the inverter operate at a high switching rate. Therefore, a high performance and also efficient torque and flux controllers are obtained, enabling a DTC solution for multilevel-inverter-powered motor drives.      

Stator resistance tuning in a stator-flux field-oriented driveusing an instantaneous hybrid flux estimator

A self-tuning control scheme for stator-flux field-oriented induction machine drives in electric vehicles operating over a wide speed range is discussed in this paper. The stator flux can be determined accurately from the terminal voltage when the machine is operating at high speed. However, at low speed, the stator resistance must be known to calculate the stator flux. The problem of calculating the stator flux accurately over the entire speed range is addressed. The rotor flux can be found from the machine speed and rotor time constant. The stator flux, at low speed, is then calculated directly from the rotor flux. By alternating between these two methods of determining the stator flux, a self-tuning operation is achieved, wherein the stator and rotor resistances are periodically updated. Since both methods of determining the stator flux are forced to track one another, a smooth transition between flux estimators is obtained. The torque and flux are then controlled in a deadbeat fashion. Good torque control over a wide speed range can therefore be obtained. With the proposed scheme, the advantages of direct torque control are obtained over the entire speed range with the addition of a speed sensor     

Sensorless double-inverter-fed wound-rotor induction-Machine drive

The basic operation of a wound-rotor induction-motor drive fed by inverters on the stator as well as the rotor side is discussed. Different modes of operations are defined and explained based on power flow on both the sides of the machine. The sensorless motor control scheme consists of V/f-type direct frequency control on one side, with either vector control or direct torque and flux control on the other side. The machine operates up to twice the rated speed in either direction, with full flux and torque, thereby producing up to twice the rated power. Novel frequency profiles are proposed, which ensure that the frequency on either side never drops below a minimum value (set at 12 Hz in this work). Therefore, the estimation of flux can be simply and reliably carried out by integration of voltage, resulting in simple sensorless control. The drive works reliably at all speeds including zero speed and at all loads. Results from a 50-hp prototype drive are presented.     

Synchronization at startup and stable rotation reversal of sensorless nonsalient PMSM drives

In this paper, a variant of the well-known "voltage model" is applied to rotor position estimation for sensorless control of nonsalient permanent-magnet synchronous motors (PMSMs). Particular focus is on a low-speed operation. It is shown that a guaranteed synchronization from any initial rotor position and stable reversal of rotation can be accomplished, in both cases under load. Stable rotation reversal is accomplished by making the estimator insensitive to the stator resistance. It is also shown that the closed-loop speed dynamics are similar to those of a sensored drive for speeds above approximately 0.1 per unit, provided that the model stator inductance is underestimated. Experimental results support the theory.     

DTFC-SVM motion-sensorless control of a PM-assisted reluctance synchronous machine as starter-alternator for hybrid electric vehicles

Permanent magnet-assisted reluctance synchronous machine (PM-RSM) starter alternator systems are credited with good performance for wide speed range in hybrid electric vehicles. This paper proposes a motion-sensorless motor/generator control of PM-RSM from zero speed up to maximum speed, using direct torque and flux control with space vector modulation. A quasioptimal stator flux reference with a flux versus torque functional is proposed. A stator flux observer in wide speed range uses combined voltage-current models for low speeds, and only the voltage model for medium to high speeds, both in proportional-integral closed loop. A novel rotor speed and position observer with a fusion strategy employs signal injection and only one D-module vector filter in stator reference for low speed, combined with a speed observer from the stator flux vector estimation-for medium-high speed. The proposed system is introduced piece by piece and then implemented on a dSpace 1103 control board with a 350-A metal-oxide-semiconductor field-effect transistor-pulse-width modulation converter connected to a 42-Vdc, 55-Ah battery, and a 140-Nm peak torque PM-RSM. Extensive experimental results from very low speed to high speed, regarding observers and drive responses, including artificial loading (motoring and generating), seem very encouraging for future starter-alternator systems.     

An implementation of a programmable cascaded low-pass filter for a rotor flux synthesizer for an induction motor drive

This paper investigates a programmable cascaded low pass filter for the estimation of rotor flux of an induction motor, with a view to estimate the rotor time constant of an indirect field orientation controlled induction motor drive. Programmable cascaded low pass filters have been traditionally used in stator flux oriented vector control of the induction motor. This paper extends the use of this filter to estimate the rotor flux for the indirect field orientation control by generating rotor flux estimates from stator flux estimates. This is achieved by using a three-stage programmable cascaded low pass filter. The three-stage programmable cascaded low-pass filter investigated in this paper has resulted in excellent estimation of rotor flux in the steady-state and transient operation of an indirect field oriented drive. The estimated rotor flux data have also been used for the on-line rotor resistance identification with artificial neural network. Modeling and experiment results presented in this paper demonstrate this method of estimating rotor flux clearly.     

Online Stator and Rotor Resistance Estimation Scheme Using Artificial Neural Networks for Vector Controlled Speed Sensorless Induction Motor Drive

This paper presents a new method of online estimation for the stator and rotor resistances of the induction motor for speed sensorless indirect vector controlled drives, using artificial neural networks. 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. For the stator resistance estimation, the error between the measured stator current and the estimated stator current using neural network is back propagated to adjust the weights of the neural network. The rotor speed is synthesized from the induction motor state equations. The performance of the stator and rotor resistance estimators and torque and flux responses of the drive, together with these estimators, are investigated with the help of simulations for variations in the stator and rotor resistances from their nominal values. Both resistances are estimated experimentally, using the proposed neural network in a vector controlled induction motor drive. Data on tracking performances of these estimators are presented. With this speed sensorless approach, the rotor resistance estimation was made insensitive to the stator resistance variations both in simulation and experiment. The accuracy of the estimated speed achieved experimentally, without the speed sensor clearly demonstrates the reliable and high-performance operation of the drive