For higher energy efficiency and greater motion control flexibility, inverters are used to drive electric motors. High-performance operation of motor drives requires complicated control algorithms and corresponding motor parameter information. Nonetheless, the users may not have the required skills or tools to obtain accurate motor data for high-performance control. Therefore, it is necessary for commercial motor drives to have highly accurate self-commissioning capability. The motor drive parameters obtained by self-commissioning are essential to design the current and speed controllers; high-frequency signal injection based sensorless control, observer-based sensorless control, and energy efficiency improvement schemes such as maximum torque per ampere (MTPA) control. Yet, established self-commissioning methods are not mature enough to provide self-sufficient, robust, and complete solution and also to fully cover newly emerging motor types such as interior permanent-magnet synchronous motors. In order to improve them, the dissertation focuses on a comprehensive study on the end-to-end selfcommissioning of general-purpose industrial sensorless ac motor drives. The dissertation targets full coverage of all motor drive parameters in a self-sufficient way through step-by-step estimation of the relevant parameters. The proposed parameter estimation methods are applicable to all AC motor types whenever the motor nature allows so. First of all, a novel method for spatial inductance map identification is proposed to estimate relevant inductance values which is used to tune the current controller. This method uses open loop voltage injection with automatic selection of injection amplitude and frequency and does not need rotor position information. After current-loop auto-tuning, a precise and robust method for initial rotor position is presented for synchronous motors. Later, estimation methods for stator resistance and saturated inductances are provided for all AC motors. Also, the nature of induction motor introduces unique difficulties in the standstill estimation of magnetizing inductance and rotor resistance. Existing traditional methods of low-frequency current injection and flux integration are not successful enough to provide robust results that can be replicated in many different motorinverter pairs. A simple novel method that makes use of the redundancy of resistive drops during dc-magnetization of the motor is presented to solve to these difficulties. Upon finishing stationary tests, rotational test is required for the estimation of mechanical parameters. Inertia estimation and consequent speed-loop auto-tuning has some challenges such as automatic test torque selection satisfying most of the practical conditions with varying mechanical inertia and loads, creating controlled speed oscillations, and robust signal detection during the test. This dissertation proposes novel solutions to these. Overall, this dissertation proposes an end-to-end, self-sufficient, and robust solution for self-commissioning of sensorless industrial AC drives.

Inhalt: The purpose of this book is to review the various schemes and methodologies used for speed sensorless operation of induction motors an position sensorless operation of permanent magnet, synchronous reluctance, and switched reluctance motors. Various sensorless control strategies are reviewed based on the papers published in IEEE transactions and conferences and in other international journals.

The book focuses on position sensorless control for PMSM drives, addressing both basic principles and experimental evaluation. It provides an in-depth study on a number of major topics, such as model-based sensorless control, saliency-based sensorless control, position estimation error ripple elimination and acoustic noise reduction. Offering a comprehensive and systematic overview of position sensorless control and practical issues it is particularly suitable for readers interested in the sensorless control techniques for PMSM drives. The book is also a valuable resource for researchers, engineers, and graduate students in fields of ac motor drives and sensorless control.

This monograph shows the reader how to avoid the burdens of sensor cost, reduced internal physical space, and system complexity in the control of AC motors. Many applications fields—electric vehicles, wind- and wave-energy converters and robotics, among them—will benefit. Sensorless AC Electric Motor Control describes the elimination of physical sensors and their replacement with observers, i.e., software sensors. Robustness is introduced to overcome problems associated with the unavoidable imperfection of knowledge of machine parameters—resistance, inertia, and so on—encountered in real systems. The details of a large number of speed- and/or position-sensorless ideas for different types of permanent-magnet synchronous motors and induction motors are presented along with several novel observer designs for electrical machines. Control strategies are developed using high-order, sliding-mode and quasi-continuous-sliding-mode techniques and two types of observer–controller schemes based on backstepping and sliding-mode techniques are described. Experimental results validate the performance of these observer and controller configurations with test trajectories of significance in difficult sensorless-AC-machine problems. Control engineers working with AC motors in a variety of industrial environments will find the space-and-cost-saving ideas detailed in Sensorless AC Electric Motor Control of much interest. Academic researchers and graduate students from electrical, mechanical and control-engineering backgrounds will be able to see how advanced theoretical control can be applied in meaningful real systems.

This is the first comprehensive book on sensorless high performance a.c. drives. It is essential reading for anyone interestred in acquiring a solid background on sensorless torque-controlled drives. It presents a detailed and unified treatment of sensorless vector-controlled and direct-torque controlled drive systems. It also discusses the applications of artificial intelligence to drives. Where possible, space vector theory is used and emphasis is laid on detailed mathematical and physical analysis. Sensorless drive schemes for different types of permanent magnet synchronous motors, synchronous reluctance motors, and induction motors are also presented. These include more than twenty vector drives e.g. five types of MRAS-based vector drives, and eleven types of direct-torque-controlled (DTC) drives, e.g. the ABB DTC drive. However, torque-controlled switched reluctance motor drives are also discussed due to their emerging importance. The book also covers various drive applications using artificial intelligence (fuzzy logic, neural networks, fuzzy-neural networks) and AI-based modelling of electrical machines. Finally, self-commissioning techniques are also discussed. This is a comprehensive thoroughly up-to-date, and self-contained book suitable for students at various levels, teachers, and industrial readership. Peter Vas is a Professor at the Department of Engineering at the University of Aberdeen, UK, where he is also the Head of the Intelligent Motion Control Group. His previous books published by Oxford University Press are extensively used worldwide.