Improved rotor flux estimation at low speeds for torque MRAS-based sensorless induction motor drives

In this paper, an improved rotor flux estimation method for the Torque model reference adaptive schemes (TMRAS) sensorless induction machine drive is proposed to enhance its performance in low and zero speed conditions. The conventional TMRAS scheme uses an open loop flux estimator and a feedforward term, with basic low pass filters replacing the pure integrators. However, the performance of this estimation technique has drawbacks at very low speeds with incorrect flux estimation significantly affecting this inherently sensorless scheme. The performance of the proposed scheme is verified by both simulated and experimental testing for an indirect vector controlled 7.5-kW induction machine. Results show the effectiveness of the proposed estimator in the low- and zero-speed regions with improved robustness against motor parameter variation compared to the conventional method

A comparison of stator flux linkage estimators for a direct torque controlled PMSM drive

In an increasing number of applications highly dynamic electrical drives, characterized by high quality torque control, are demanded. Direct torque control (DTC) for AC machines, permanent magnet synchronous motors (PMSM) or induction machines, can provide this accurate and fast torque control. When applying DTC the change of the stator flux linkage vector is controlled, based on torque and flux errors. As such the estimation of the stator flux linkage is essential for a DTC drive. Furthermore the quality of the estimation directly determines the capability of the drive. In the literature several possible solutions for the estimation of the stator flux linkage are proposed. However, a comprehensive comparison between these solutions is not present. This paper gives an overview of several techniques for the estimation of the stator flux linkage for DTC in PMSMs. The theoretical advantages and disadvantages of the methods are outlined. After a short discussion on the effects of erroneous estimations the results from simulations for the different methods are reviewed. It is shown that, despite their simplicity stabilized voltage model methods can offer good performance. Still they can not reach the performance of an extended Kalman filter implementation of a current model. Aspects of the practical implementation on FPGA are discussed

A Discrete-Time Direct-Torque Control for Direct-Drive PMSG-Based Wind Energy Conversion Systems

This paper proposes a novel flux space vector-based direct-torque control (DTC) scheme for permanent magnet synchronous generators (PMSGs) used in variable-speed direct drive wind energy conversion systems (WECSs). The discrete time control law, which is derived from the perspective of flux space vectors and load angle, predicts the desired stator flux vector for the next time-step with the torque and stator flux information only. The space-vector modulation (SVM) is then employed to generate the reference voltage vector, leading to a fixed switching frequency as well as lower flux and torque ripples when compared to the conventional DTC. Compared with other SVM-based DTC methods in the literature, the proposed DTC scheme eliminates the use of PI regulators and is less dependent on machine parameters, e.g., stator inductances and permanent magnet flux linkage, while the main advantages of the DTC, e.g., fast dynamic response and no need of coordinate transform, are preserved. The proposed DTC scheme is applicable for both nonsalient-pole and salient-pole PMSGs. The overall control scheme is simple to implement and is robust to parameter uncertainties and variations of the PMSGs. The effectiveness of the proposed discrete-time DTC scheme is verified by simulation and experimental results on a 180 W salient-pole PMSG and a 2.4-kW nonsalient-pole PMSG used in variable-speed direct-drive WECSs

Sensorless control for limp-home mode of EV applications

PhD ThesisOver the past decade research into electric vehicles’ (EVs) safety, reliability and availability has become a hot topic and has attracted a lot of attention in the literature. Inevitably these key areas require further study and improvement. One of the challenges EVs face is speed/position sensor failure due to vibration and harsh environments. Wires connecting the sensor to the motor controller have a high likelihood of breakage. Loss of signals from the speed/position sensor will bring the EV to halt mode. Speed sensor failure at a busy roundabout or on a high speed motorway can have serious consequences and put the lives of drivers and passengers in great danger. This thesis aims to tackle the aforementioned issues by proposing several novel sensorless schemes based on Model Reference Adaptive Systems (MRAS) suitable for limp-home mode of EV applications. The estimated speed from these schemes is used for the rotor flux position estimation. The estimated rotor flux position is employed for sensorless torque-controlled drive (TCD) based on indirect rotor field oriented control (IRFOC). The capabilities of the proposed schemes have been evaluated and compared to the conventional back-Electromotive Force MRAS (back-EMF MRAS) scheme using simulation environment and a test bench setup. The new schemes have also been tested on electric golf buggies. The results presented for the proposed schemes show that utilising these schemes provide a reliable and smooth sensorless operation during vehicle test-drive starting from standstill and over a wide range of speeds, including the field weakening region. Employing these new schemes for sensorless TCD in limp-home mode of EV applications increases safety, reliability and availability of EVs

A simple method to reduce torque ripple in direct torque-controlled permanent-magnet synchronous motor by using vectors with variable amplitude and angle

In this paper, a modified direct torque control (DTC) for permanent-magnet synchronous machines, which enables important torque- and flux-ripple reduction by using voltage vectors with variable amplitude and angle, is proposed. In the proposed DTC, the amplitudes of torque and flux errors are differentiated and employed to regulate the amplitude and angle of the output voltage vectors online, which are finally synthesized by space-vector modulation (SVM). Two simple formulas are developed to derive the amplitude and angle of the commanding voltage vectors from the errors of torque and flux only. The conventional switching table and hysteresis controllers are eliminated, and a fixed switching frequency is obtained with the help of SVM. Stator flux is estimated from an improved voltage model, which is based on a low-pass filter with compensations of the amplitude and phase. The proposed DTC is comparatively investigated with the existing SVM-DTC from the aspects of theory analysis, computer simulation, and experimental validation. The simulation and experimental results prove that the proposed DTC is very simple and provides excellent steady-state response, quick dynamic performance, and strong robustness against external disturbance and control-parameter variations. © 2006 IEEE

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

Artificial intelligence applied to speed sensorless induction motor drives

During the last two decades there has been considerable development of sensorless vector controlled induction motor drives for high performance industrial applications. Such control strategies reduce the drive's cost, size and maintenance requirements while increasing the system's reliability and robustness. Parameter sensitivity, high computational effort and instability at low and zero speed can be the main shortcomings of sensorless control. Sensorless drives have been successfully applied for medium and high speed operation, but low and zero speed operation is still a critical problem. Much recent research effort is focused on extending the operating region of sensorless drives near zero stator frequency. Several strategies have been proposed for rotor speed estimation in sensorless induction motor drives based on the machine fundamental excitation model. Among these techniques Model Reference Adaptive Systems (MRAS) schemes are the most common strategies employed due to their relative simplicity and low computational effort. Rotor flux-MRAS is the most popular MRAS strategy and significant attempts have been made to improve the performance of this scheme at low speed. Artificial Intelligence (AI) techniques have attracted much attention in the past few years as powerful tools to solve many control problems. Common AI strategies include neural networks, fuzzy logic and genetic algorithms. The mam purpose of this work is to show that AI can be used to improve the sensorless performance of the well-established MRAS observers in the critical low and zero speed region of operation. This thesis proposes various novel methods based on AI combined with MRAS observers. These methods have been implemented via simulation but also on an experimental drive based around a commercial induction machine. Detailed simulations and experimental tests are carried out to investigate the performance of the proposed schemes when compared to the conventional rotor fluxMRAS. Various schemes are implemented and tested in real time using a 7.5 kW induction machine and a dSP ACE DS 1103 controller board. The results presented for these new schemes show the great improvement in the performance of the MRAS observer in both open loop and sensorless modes of operation at low and zero speed.EThOS - Electronic Theses Online ServiceMinistry of Higher Education, Arab Republic of EgyptGBUnited Kingdo

Advances in Rotating Electric Machines

It is difficult to imagine a modern society without rotating electric machines. Their use has been increasing not only in the traditional fields of application but also in more contemporary fields, including renewable energy conversion systems, electric aircraft, aerospace, electric vehicles, unmanned propulsion systems, robotics, etc. This has contributed to advances in the materials, design methodologies, modeling tools, and manufacturing processes of current electric machines, which are characterized by high compactness, low weight, high power density, high torque density, and high reliability. On the other hand, the growing use of electric machines and drives in more critical applications has pushed forward the research in the area of condition monitoring and fault tolerance, leading to the development of more reliable diagnostic techniques and more fault-tolerant machines. This book presents and disseminates the most recent advances related to the theory, design, modeling, application, control, and condition monitoring of all types of rotating electric machines