Parameter estimation for condition monitoring of PMSM stator winding and rotor permanent magnets

Winding resistance and rotor flux linkage are important to controller design and condition monitoring of a surface-mounted permanent-magnet synchronous machine (PMSM) system. In this paper, an online method for simultaneously estimating the winding resistance and rotor flux linkage of a PMSM is proposed, which is suitable for application under constant load torque. It is based on a proposed full-rank reference/variable model. Under constant load torque, a short pulse of id 0 is transiently injected into the d-axis current, and two sets of machine rotor speeds, currents, and voltages corresponding to id = 0 and id 0 are then measured for estimation. Since the torque is kept almost constant during the transient injection, owing to the moment of system inertia and negligible reluctance torque, the variation of rotor flux linkage due to injected id 0 can be taken into account by using the equation of constant torque without measuring the load torque and is then associated with the two sets of machine equations for simultaneously estimating the winding resistance and rotor flux linkage. Furthermore, the proposed method does not need the values of the $dq$-axis inductances, while the influence from the nonideal voltage measurement, which will cause an ill-conditioned problem in the estimation, has been taken into account and solved by error analysis. This method is finally verified on two prototype PMSMs and shows good performance. © 1982-2012 IEEE

Dynamic Performance Analysis of a Five-Phase PMSM Drive Using Model Reference Adaptive System and Enhanced Sliding Mode Observer

This paper aims to evaluate the dynamic performance of a five-phase PMSM drive using two different observers: sliding mode (SMO) and model reference adaptive system (MRAS). The design of the vector control for the drive is firstly introduced in details to visualize the proper selection of speed and current controllers’ gains, then the construction of the two observers are presented. The stability check for the two observers are also presented and analyzed, and finally the evaluation results are presented to visualize the features of each sensorless technique and identify the advantages and shortages as well. The obtained results reveal that the de-signed SMO exhibits better performance and enhanced robustness compared with the MRAS under different operating conditions. This fact is approved through the obtained results considering a mismatch in the values of stator resistance and stator inductance as well. Large deviation in the values of estimated speed and rotor position are observed under MRAS, and this is also accompanied with high speed and torque oscillations

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

GPU Implementation of DPSO-RE Algorithm for Parameters Identification of Surface PMSM Considering VSI Nonlinearity

In this paper, an accurate parameter estimation model of surface permanent magnet synchronous machines (SPMSMs) is established by taking into account voltage-source-inverter (VSI) nonlinearity. A fast dynamic particle swarm optimization (DPSO) algorithm combined with a receptor editing (RE) strategy is proposed to explore the optimal values of parameter estimations. This combination provides an accelerated implementation on graphics processing unit (GPU), and the proposed method is, therefore, referred to as G-DPSORE. In G-DPSO-RE, a dynamic labor division strategy is incorporated into the swarms according to the designed evolutionary factor during the evolution process. Two novel modifications of the movement equation are designed to update the velocity of particles. Moreover, a chaotic-logistic-based immune RE operator is developed to facilitate the global best individual (gBest particle) to explore a potentially better region. Furthermore, a GPU parallel acceleration technique is utilized to speed up parameter estimation procedure. It has been demonstrated that the proposed method is effective for simultaneous estimation of the PMSM parameters and the disturbance voltage (Vdead) due to VSI nonlinearity from experimental data for currents and rotor speed measured with inexpensive equipment. The influence of the VSI nonlinearity on the accuracy of parameter estimation is analyzed

High Frequency Injection Sensorless Control for a Permanent Magnet Synchronous Machine Driven by an FPGA Controlled SiC Inverter

As motor drive inverters continue to employ Silicon Carbide (SiC) and Gallium Nitride (GaN) devices for power density improvements, sensorless motor control strategies can be developed with field-programmable gate arrays (FPGA) to take advantage of high inverter switching frequencies. Through the FPGA’s parallel processing capabilities, a high control bandwidth sensorless control algorithm can be employed. Sensorless motor control offers cost reductions through the elimination of mechanical position sensors or more reliable electric drive systems by providing additional position and speed information of the electric motor. Back electromotive force (EMF) estimation or model-based methods used for motor control provide precise sensorless control at high speeds; however, they are unreliable at low speeds. High frequency injection (HFI) sensorless control demonstrates an improvement at low speeds through magnetic saliency tracking. In this work, a sinusoidal and square-wave high frequency injection sensorless control method is utilized to examine the impact an interior permanent magnet synchronous machine’s (IPMSM) fundamental frequency, injection frequency, and switching frequency have on the audible noise spectrum and electrical angle estimation. The audible noise and electrical angle estimation are evaluated at different injection voltages, injection frequencies, switching frequencies, and rotor speeds. Furthermore, a proposed strategy for selecting the proper injection frequency, injection voltage, and switching frequency is given to minimize the electrical angle estimation error

A PI/Backstepping Approach for Induction Motor Drives Robust Control

International audienceThis paper presents a robust control design procedure for induction motor drives in case of modeling errors and unknown load torque. The control law is based on the combination of nonlinear PI controllers and a backstepping methodology. More precisely, the controllers are determined by imposing flux-speed tracking in two steps and by using appropriate PI gains that are nonlinear functions of the system state. A comparative study between the proposed PI/Backstepping approach and the feedback linearizing control is made by realistic simulations including load torque changes, parameter variations and measurement noises. Flux-speed tracking results show the proposed method effectiveness in presence of strong disturbances

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

Speed Sensorless Induction Motor Drive Control for Electric Vehicles

Fast diminishing fossil fuel resources, deterioration in air quality and concerns for environmental protection, continuously promote the interest in the research and development of Alternative Energy Vehicles (AEVs). Traction motor drive is an integral part and common electric propulsion system in all kinds of AEVs. It plays an utmost significant role in the development of electrified transport industry. Application of Induction Motor (IM) drive is not only limited to the domestic and industrial applications but also has an ubiquitous influence in the modern electrified transport sector. IM is characterized by a simple and rugged structure, operational reliability, low maintenance, low cost, ability to operate in a hostile environment and high dynamic performance. However, IM is one of the widely accepted choices by Electric Vehicles (EVs) manufacturer. At present, Variable speed IM drive is almost replacing the traditional DC motor drive in a wide range of applications including EVs where a fast dynamic response is required. It became possible after the technological advancement and development in the field of power switching devices, digital signal processing and recently intelligent control systems have led to great improvements in the dynamic performance of traction drives. Speed Sensorless control strategies offer better system’s reliability and robustness and reduce the drive cost, size and maintenance requirements. Sensorless IM drives have been applied on medium and high speed applications successfully. However, instability at low speed and under different load disturbance conditions are still a critical problem in this research field and has not been robustly achieved. Some application such as traction drives and cranes are required to maintain the desired level of torque down to low speed levels with uncertain load torque disturbance conditions. Speed and torque control is more important particularly in motor-in-wheel traction drive train configuration EVs where vehicle wheel rim is directly connected to the motor shaft to control the speed and torque. The main purpose of this research is to improve the dynamic performance of conventional proportional-integral controller based model reference adaptive system (PI-MRAS) speed observer by using several speed profiles under different load torque disturbance conditions, which is uncertain during the whole vehicle operation apart from the vehicle own load. Since, vehicle has to face different road conditions and aerodynamic effects which continuously change the net load torque effect on the traction drive. This thesis proposes different novel methods based on the fuzzy logic control (FLC) and sliding mode control (SMC) with rotor flux MRAS. Numerous simulations and experimental tests designed with respect to the EV operation are carried out to investigate the speed estimation performance of the proposed schemes and compared with the PI-MRAS speed observer. For simulation and experimental purpose, Matlab-Simulink environment and dSPACE DS-1104 controller board are used respectively. The results presented in this thesis show great performance improvements of the proposed schemes in speed estimation & load disturbance rejection capability and provide a suitable choice of speed sensoless IM drive control for EVs with cost effectiveness