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

Sensorless Control of Switched-Flux Permanent Magnet Machines

This thesis investigates the sensorless control strategies of permanent magnet synchronous machines (PMSMs), with particular reference to switched-flux permanent magnet (SFPM) machines, based on high-frequency signal injection methods for low speed and standstill and the back-EMF based methods for medium and high speeds

Rotor Position Identification for Brushless DC motor

Permanent magnet BLDC motors are characterized by a central magnetic core, called the rotor, and fixed electric coils (usually six) equally spaced in a ring around the core, called the stator. Motor movement is controlled by alternately energizing and de-energizing the stator coils to create a rotating magnetic field that propels the rotor. In order for this process to work correctly, BLDC motors required a technology called electronic commutation, in which the coil currents must be very carefully synchronized to rotor position to ensure that the rotating field is correctly aligned with the permanent magnetic field in the rotor. Usually rotor position is measured by external sensors such as Hall-effect sensors and optical encoders and these external sensors increase the system cost and reduces reliability. In order to control the price and make it more reliable this thesis propose to infer the rotor position from voltage and current measurement of motor. The most common approaches to sensorless control are based on the measurement of the electromotive force (back-EMF), that is induced by the rotor motion. As the back-EMF is nearly zero at very low speed and at stationary position, and can not be measured. Therefore a separate algorithm is required for start-up and control at low speed. The other method of sensorless control involves the inference of rotor position from the variation in inductance caused by rotor position. This thesis presents a prototype system for sensorless control of BLDC motors over the entire speed range of the motor, including stall (zero speed) conditions using the voltage and current signals from the motor

Optimal Sensorless Four Switch Direct Power Control of BLDC Motor

Brushless DC (BLDC) motors are used in a wide range of applications due to their high efficiency and high power density. In this paper, sensorless four-switch direct power control (DPC) method with the sector to sector commutations ripple minimization for BLDC motor control is proposed. The main features of the proposed DPC method are: (1) fast dynamic response (2) easy implementation (3) use of power feedback for motor control that is much easy to implement (4) eliminating the torque dips during sector-to sector commutations. For controlling the motor speed, a position sensorless method is used enhancing drive reliability. For reference speed tracking, a PI control is also designed and tuned based on imperialist competition algorithm (ICA) that reduces reference tracking error. The feasibility of the proposed control method is developed and analyzed by MATLAB/SIMULINK®. Simulation results prove high performance exhibited by the proposed DPC strategy

Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine

Optimal performance of the electric machine/drive system is mandatory to improve the energy consumption and reliability. To achieve this goal, mathematical models of the electric machine/drive system are necessary. Hence, this motivated the editors to instigate the Special Issue “Mathematical Approaches to Modeling, Optimally Designing, and Controlling Electric Machine”, aiming to collect novel publications that push the state-of-the art towards optimal performance for the electric machine/drive system. Seventeen papers have been published in this Special Issue. The published papers focus on several aspects of the electric machine/drive system with respect to the mathematical modelling. Novel optimization methods, control approaches, and comparative analysis for electric drive system based on various electric machines were discussed in the published papers

Torque Control

This book is the result of inspirations and contributions from many researchers, a collection of 9 works, which are, in majority, focalised around the Direct Torque Control and may be comprised of three sections: different techniques for the control of asynchronous motors and double feed or double star induction machines, oriented approach of recent developments relating to the control of the Permanent Magnet Synchronous Motors, and special controller design and torque control of switched reluctance machine

Design and Control of Electrical Motor Drives

Dear Colleagues, I am very happy to have this Special Issue of the journal Energies on the topic of Design and Control of Electrical Motor Drives published. Electrical motor drives are widely used in the industry, automation, transportation, and home appliances. Indeed, rolling mills, machine tools, high-speed trains, subway systems, elevators, electric vehicles, air conditioners, all depend on electrical motor drives.However, the production of effective and practical motors and drives requires flexibility in the regulation of current, torque, flux, acceleration, position, and speed. Without proper modeling, drive, and control, these motor drive systems cannot function effectively.To address these issues, we need to focus on the design, modeling, drive, and control of different types of motors, such as induction motors, permanent magnet synchronous motors, brushless DC motors, DC motors, synchronous reluctance motors, switched reluctance motors, flux-switching motors, linear motors, and step motors.Therefore, relevant research topics in this field of study include modeling electrical motor drives, both in transient and in steady-state, and designing control methods based on novel control strategies (e.g., PI controllers, fuzzy logic controllers, neural network controllers, predictive controllers, adaptive controllers, nonlinear controllers, etc.), with particular attention to transient responses, load disturbances, fault tolerance, and multi-motor drive techniques. This Special Issue include original contributions regarding recent developments and ideas in motor design, motor drive, and motor control. The topics include motor design, field-oriented control, torque control, reliability improvement, advanced controllers for motor drive systems, DSP-based sensorless motor drive systems, high-performance motor drive systems, high-efficiency motor drive systems, and practical applications of motor drive systems. I want to sincerely thank authors, reviewers, and staff members for their time and efforts. Prof. Dr. Tian-Hua Liu Guest Edito

Current commutation and control of brushless direct current drives using back electromotive force samples

Brushless DC machines (BLDC) are widely used in home, automotive, aerospace and military applications. The reason of this interest in different industries in this type of machine is due to their significant advantages. Brushless DC machines have a high power density, simple construction and higher efficiency compared to conventional AC and DC machines and lower cost comparing to permanent magnet AC synchronous machines. The phase currents of a BLDC machine have to commutate properly which is realised by using power semiconductors. For a proper commutation the rotor position is often obtained by an auxiliary instrument, mostly an arrangement of three Hall-effect sensors with 120 spatial displacement. In modern and cost-effective BLDC drives the focus is on replacing the noise sensitive and less reliable mechanical sensors by numerical algorithms, often referred to as sensorless or self-sensing methods. The advantage of these methods is the use of current or voltage measurements which are usually available as these are required for the control of the drive or the protection of the semiconductor switches. Avoiding the mechanical position sensor yields remarkable savings in production, installation and maintenance costs. It also implies a higher power to volume ratio and improves the reliability of the drive system. Different self-sensing techniques have been developed for BLDC machines. Two algorithms are proposed in this thesis for self-sensing commutation of BLDC machines using the back-EMF samples of the BLDC machine. Simulations and experimental tests as well as mathematical analysis verify the improved performance of the proposed techniques compared to the conventional back-EMF based self-sensing commutation techniques. For a robust BLDC drive control algorithm with a wide variety of applications, load torque is as a disturbance within the control-loop. Coupling the load to the motor shaft may cause variations of the inertia and viscous friction coefficient besides the load variation. Even for a drive with known load torque characteristics there are always some unmodelled components that can affect the performance of the drive system. In self-sensing controlled drives, these disturbances are more critical due to the limitations of the self-sensing algorithms compared to drives equipped with position sensors. To compensate or reject torque disturbances, control algorithms need the information of those disturbances. Direct measurement of the load torque on the machine shaft would require another expensive and sensitive mechanical sensor to the drive system as well as introducing all of the sensor related problems to the drive. An estimation algorithm can be a good alternative. The estimated load torque information is introduced to the self-sensing BLDC drive control loop to increase the disturbance rejection properties of the speed controller. This technique is verified by running different experimental tests within different operation conditions. The electromagnetic torque in an electrical machine is determined by the stator current. When considering the dynamical behaviour, the response time of this torque on a stator voltage variation depends on the electric time constant, while the time response of the mechanical system depends on the mechanical time constant. In most cases, the time delays in the electric subsystem are negligible compared to the response time of the mechanical subsystem. For such a system a cascaded PI speed and current control loop is sufficient to have a high performance control. However, for a low inertia machine when the electrical and mechanical time constants are close to each other the cascaded control strategies fail to provide a high performance in the dynamic behavior. When two cascade controllers are used changes in the speed set-point should be applied slowly in order to avoid stability problems. To solve this, a model based predictive control algorithm is proposed in this thesis which is able to control the speed of a low inertia brushless DC machine with a high bandwidth and good disturbance rejection properties. The performance of the proposed algorithm is evaluated by simulation and verified by experimental results as well. Additionally, the improvement on the disturbance rejection properties of the proposed algorithm during the load torque variations is studied. In chapters 1 and 2 the basic operation principles of the BLDC machine drives will be introduced. A short introduction is also given about the state of the art in control of BLDC drives and self-sensing control techniques. In chapter 3, a model for BLDC machines is derived, which allows to test control algorithms and estimators using simulations. A further use of the model is in Model Based Predictive Control (MBPC) of BLDC machines where a discretised model of the BLDC machine is implemented on a computation platform such as Field Programmable Gate Arrays (FPGA) in order to predict the future states of the machine. Chapter 4 covers the theory behind the proposed self-sensing commutation methods where new methodologies to estimate the rotor speed and position from back-EMF measurements are explained. The results of the simulation and experimental tests verifies the performance of the proposed position and speed estimators. It will also be proved that using the proposed techniques improve the detection accuracy of the commutation instants. In chapter 5, the focus is on the estimation of load torque, in order to use it to improve the dynamic performance of the self-sensing BLDC machine drives. The load torque information is used within the control loop to improve the disturbance rejection properties of the speed control for the disturbances resulting from the applied load torque of the machine. Some of the machine parameters are used within speed and load torque estimators such as back-EMF constant Ke and rotor inertia J. The accuracy with which machine parameters are known is limited. Some of the machine parameters can change during operation. Therefore, the influence of parameter errors on the position, speed and load torque is examined in chapter 5. In Chapter 6 the fundamentals of Model based Predictive Control for a BLDC drive is explained, which are then applied to a BLDC drive to control the rotor speed. As the MPC algorithm is computationally demanding, some enhancements on the FPGA program is also introduced in order to reduce the required resources within the FPGA implementation. To keep the current bounded and a high speed response a specific cost function is designed to meet the requirements. later on, the proposed MPC method is combined with the proposed self-sensing algorithm and the advantages of the combined algorithms is also investigated. The effects of the MPC parameters on the speed and current control performance is also examined by simulations and experiments. Finally, in chapter 7 the main results of the research is summarized . In addition, the original contributions that is give by this work in the area of self-sensing control is highlighted. It is also shown how the presented work could be continued and expanded

Design and control of a synchronous reluctance machine drive

This thesis investigates the design, performance and control of a synchronous reluctance machine (Synchrel) drive. The Synchrel machine is proposed for variable speed drives because of its advantages over other machines. The rotor has no cage winding, brushes or slip rings. The torque ripple levels are lower in the Synchrel machine than the switched reluctance machine as it operates from a standard sine wave supply. An axially laminated rotor was designed based on finite element analysis, with the aim of producing the same output power as obtained from an induction motor (M) with a similar stator. Using vector control, the developed torque is controlled by regulating the stator current vector. Two vector control schemes are used, maximum torque per ampere and constant current in the direct axis. The output torque characteristics of the machine have been confirmed by finite element analysis. Slotine's approach of sliding mode control is used for position control of the vector controlled synchronous reluctance machine. A comparison is undertaken between the performance of a fixed gain controller with two sliding mode controllers, for both the regulator and servo cases. Invariant performance is obtained using Slotine's sliding mode control approach, unlike with a fixed gain controller. Robustness to parameter variation is an important feature of this technique. This robustness can be achieved through the control law design, assuming parameter variation bounds are known. These improvements are demonstrated for variations in load inertia. Inductance ripple affects machine performance, for example decreasing output torque and increasing core losses. A state space model for the machine that incorporates this inductance effect, yields drive simulation results that agree with experimental results

Modelling and practical set-up to investigate the performance of permanent magnet synchronous motor through rotor position estimation at zero and low speeds

This thesis provides a study for the rotor position estimation in SM-PMSMs, particularly at zero and low speeds. The method for zero rotor speed is based on injection of three high frequency voltage pulses in the motor stator windings. Then, the voltage responses at the motor terminals are exploited to extract the rotor position. Two approaches, modelling and practical implementations, are presented. The obtained results have showed a verification of a high-resolution position estimation (a position estimation of 1 degree angle), a simplicity and cost effective implementation and a no need for current sensors is required to achieve the estimation process. It should be noticed that the implementation of rotor position estimation at zero speed is only attended when the rotor is at standstill or very low speed. Therefore, the motor driver is not expected to be active at this condition. Thereby, the zero speed estimation does not provide a robust torque control. In future, this should be taking into consideration to overcome this drawback and to make the estimator more reliable. At low speed running, the primary goal is to start spinning the under test motors, and then the rotor position estimation is achieved. The motor spinning is based on adopting a virtual injected signal to generate the voltage components, Vα and Vβ, of the space vector pulse width modulation technique. Then, generating the eight space vectors is conducted through storing the standard patterns of the six space vector sectors in a memory structure together with the timing sequences of each sector. The presented strategy of motor running includes a proposed motor speed control scheme, which is based on controlling the frequency of the power signal, at the inverter output, through controlling the timing period of execution the power delivery program. The thesis presents a proposed method to achieve the estimation goal depends on tracking the magnetic saliency on one motor line voltage. Thereby, the rotor position estimation The introduced proposed method, for rotor position estimation at zero speed, verifies the following contributions: - Presents a simple and cost effective zero speed rotor position estimator for the motor under test. - The aimed resolution in this thesis is an angle 1 degree. IV - Adopting solely the measuring of motor terminal voltages. Eliminating the detection of the rotor magnet polarity as a necessary technique for completing the position estimation. At low speed running, the following contributions are verified: - Rather than a real frequency signal, a virtual injected signal is adopted to generate the voltage components, Vα and Vβ of the space vector pulse width modulation technique. - The proposed method for generating the eight space vectors is based on storing the standard patterns of the six sectors in a memory structure together with the timing sequence. - The strategy of motor speed control is based on controlling the period of execution the power delivery program. - The strategy of low speed rotor position employs one motor line voltage from which the low speed estimation is achieved