Influence of nonideal voltage measurement on parameter estimation in permanent-magnet synchronous machines

This paper investigates the influence of nonideal voltage measurements on the parameter estimation of permanentmagnet synchronous machines (PMSMs). The influence of nonideal voltage measurements, such as the dc bus voltage drop, zero shift in the amplifier, and voltage source inverter nonlinearities, on the estimation of different machine parameters is investigated by theoretical and experimental analysis. For analysis, a model-reference-adaptive-system-based estimator is first described for the parameter estimation of the q-axis inductance, stator winding resistance, and rotor flux linkage. The estimator is then applied to a prototype surface-mounted PMSM to investigate the influence of nonideal voltage measurement on the estimation of various machine parameter values. It shows that, at low speed, the inverter nonlinearity compensation has significant influence on both the rotor flux linkage and winding resistance estimations while, at high speed, it has significant influence only on the winding resistance estimation and has negligible influence on the rotor flux linkage estimation. In addition, the inverter nonlinearity compensation will not influence the q-axis inductance estimation when it is under id = 0 control. However, the dc bus voltage drop due to the load variation and zero shift in the amplifier will significantly influence the q-axis inductance estimation. © 2011 IEEE

High-frequency issues using rotating voltage injections intended for position self-sensing

The rotor position is required in many control schemes in electrical drives. Replacing position sensors by machine self-sensing estimators increases reliability and reduces cost. Solutions based on tracking magnetic anisotropies through the monitoring of the incremental inductance variations are efficient at low-speed and standstill operations. This inductance can be estimated by measuring the response to the injection of high-frequency signals. In general however, the selection of the optimal frequency is not addressed thoroughly. In this paper, we propose discrete-time operations based on a rotating voltage injection at frequencies up to one third of the sampling frequency used by the digital controller. The impact on the rotation-drive, the computational requirement, the robustness and the effect of the resistance on the position estimation are analyzed regarding the signal frequency

A new simplified fundamental model‐based sensorless control method for surface‐mounted permanent magnet synchronous machines

For sensorless control of surface‐mounted permanent magnet synchronous machines (SPMSMs), the major issue is in zero‐ and low‐speed ranges. Since back‐electromotive force (EMF) is proportional to speed, back‐EMF based methods fail at zero and low speed. A solution considering the starting process and low speed sensorless control is presented. A simplified fundamental model‐based method is proposed. Based on the simplified model, the measured stator currents in the stationary reference frame can be directly utilised for position estimation so that the sensorless control performance at low speed and starting is improved. Moreover, with the knowledge of rotor initial position sector information, a stable and reliable starting performance is achieved with the proposed method. The effectiveness of the proposed method is verified through experimental results

Unified Field Oriented Controlled Drive System for All Types of PMSMs Considering System Nonlinearities

Permanent Magnet Synchronous Machines (PMSM) have increasing popularity in recent years due to their extensive use in domestic appliances, electric/hybrid vehicles, wind power generation and more electric aircraft technologies. This paper proposes a unified drive system simulation for all types of PMSMs. Its unified structure achieves self controller tuning and decoupling compensation once a machine is replaced by another. Field oriented control based realistic drive is implemented with a much-simplified simulation. The proposed structure incorporates with parameter variations, inverter nonlinearities, and DC-link voltage variations as well as it simulates ideal system behavior. Each system nonlinearity can be simply studied for any machine by deliberately altering the corresponding parameter owing to its unified structure. Hence, the effect of that particular variation on harmonic distortions, torque ripples, torque production capability, battery utilization ratio, system efficiency, system response and so on can be analyzed in detail. Thus, the novel implementation strategy will be quite useful to analyze the system behavior under different evaluation metrics, and it will accelerate the research and developments on the promising topic. The effectiveness of the strategy has been verified by extensive simulations

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

Parameter estimation for VSI-Fed PMSM based on a dynamic PSO with learning strategies

© 1986-2012 IEEE.A dynamic particle swarm optimization with learning strategy (DPSO-LS) is proposed for key parameter estimation for permanent magnet synchronous machines (PMSMs), where the voltage-source inverter (VSI) nonlinearities are taken into account in the parameter estimation model and can be estimated simultaneously with other machine parameters. In the DPSO-LS algorithm, a novel movement modification equation with variable exploration vector is designed to effectively update particles, enabling swarms to cover large areas of search space with large probability and thus the global search ability is enhanced. Moreover, a Gaussian-distribution-based dynamic opposition-based learning strategy is developed to help the pBest jump out local optima. The proposed DPSO-LS can significantly enhance the estimator model accuracy and dynamic performance. Finally, the proposed algorithm is applied to multiple parameter estimation including the VSI nonlinearities of a PMSM. The performance of DPSO-LS is compared with several existing PSO algorithms, and the comparison results show that the proposed parameters estimation method has better performance in tracking the variation of machine parameters effectively and estimating the VSI nonlinearities under different operation conditions

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