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

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

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

Online parameter estimation for permanent magnet synchronous machines : an overview

Online parameter estimation of permanent magnet synchronous machines is critical for improving their control performance and operational reliability. This paper provides an overview of the recent achievements of online parameter estimation of PMSMs with examples. The critical issues in parameter estimation are firstly analysed, especially the rank-deficient issue and inverter nonlinearities. Then, the state-of-the-art online parameter estimation modelling techniques are reviewed and assessed. Finally, some typical applications and examples are outlined, e.g. estimation of mechanical parameters, improvement of sensored and sensorless control performance, thermal condition monitoring, and fault diagnosis, together with future research trends

Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO

A global parameter estimation method for a PMSM drive system is proposed, where the electrical parameters, mechanical parameters and voltage-source-inverter (VSI) nonlinearity are regarded as a whole and parameter estimation is formulated as a single parameter optimization model. A dynamic learning estimator is proposed for tracking the electrical parameters, mechanical parameters and VSI of PMSM drive by using dynamic self learning particle swarm optimization (DSLPSO). In DSLPSO, a novel movement modification equation with dynamic exemplar learning strategy is designed to ensure its diversity and achieve a reasonable tradeoff between the exploitation and exploration during the search process. Moreover, a nonlinear multi-scale based interactive learning operator is introduced for accelerating the convergence speed of the Pbest particles; meanwhile a dynamic opposition-based learning (OBL) strategy is designed to facilitate the gBest particle to explore a potentially better region. The proposed algorithm is applied to parameter estimation for a PMSM drive system. The results show that the proposed method has better performance in tracking the variation of electrical parameters, and estimating the immeasurable mechanical parameters and the VSI disturbance voltage simultaneously

Improved position offset based parameter determination of permanent magnet synchronous machines under different load conditions

© The Institution of Engineering and Technology 2017.This study proposes a novel method for the parameter determination of permanent magnet (PM) synchronous machines under different load conditions. It can identify the total dq-axis flux linkages and also the PM flux linkage separately by the addition of a pair of negative and positive position offsets. It is also noteworthy that the influence of uncertain inverter non-linearity and winding resistance is cancelled during the modelling process, and the experimental results on two different PM synchronous machines show a good agreement with the finite-element prediction results. More importantly, it shows good performance in online tracking the variation of PM flux linkage, which is an important feature for aiding the condition monitoring of PMs, for example, monitoring the temperature of PMs

Identification and Adaptive Control for High-performance AC Drive Systems.

High-performance AC machinery and drive systems can be found in a variety of applications ranging from motion control to vehicle propulsion. However, machine parameters can vary significantly with electrical frequency, flux levels, and temperature, degrading the performance of the drive system. While adaptive control techniques can be used to estimate machine parameters online, it is sometimes desirable to estimate certain parameters offline. Additionally, parameter identification and control are typically conflicting objectives with identification requiring plant inputs which are rich in harmonics, and control objectives often consisting of regulation to a constant set-point. In this dissertation, we present research which seeks to address these issues for high-performance AC machinery and drive systems. The first part of this dissertation concerns the offline identification of induction machine parameters. Specifically, we have developed a new technique for induction machine parameter identification which can easily be implemented using a voltage-source inverter. The proposed technique is based on fitting steady-state experimental data to the circular stator current locus in the stator flux linkage reference-frame for varying steady-state slip frequencies, and provides accurate estimates of the magnetic parameters, as well as the rotor resistance and core loss conductance. Experimental results for a 43 kW induction machine are provided which demonstrate the utility of the proposed technique by characterizing the machine over a wide range of flux levels, including magnetic saturation. The remainder of this dissertation concerns the development of generalizable design methodologies for Simultaneous Identification and Control (SIC) of overactuated systems via case studies with Permanent Magnet Synchronous Machines (PMSMs). Specifically, we present different approaches to the design of adaptive controllers for PMSMs which exploit overactuation to achieve identification and control objectives simultaneously. The first approach utilizes a disturbance decoupling control law to prevent the excitation input from perturbing the regulated output. The second approach uses a Lyapunov-based adaptive controller to constrain the states to the output error-zeroing manifold on which they are varied to provide excitation for parameter identification. Finally, a receding-horizon control allocation approach is presented which includes a metric for generating persistently exciting reference trajectories.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120862/1/davereed_1.pd

PMSM Parameter Estimation for Sensorless FOC based on differential power factor

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksDuring the last years, different methods for identifying permanent magnet synchronous motor (PMSM) parameters have been developed. Such methods allow a better characterization of PMSMs, thus enabling a better control. This article presents a novel PMSM parameter estimation method based on the differential power factor due to the harmonic distortion, which allows the identification of the motor parameters from data acquisitions representing the entire torque-speed range. This method does not require measuring any geometric parameters, thus avoiding motor disassembly, or prior knowledge of the applied field oriented control (FOC) strategy. It also enables identifying the current, voltage, and dq components of the flux linkage without knowing the rotor position. The proposed method is based on a dq electrical model that considers the harmonic components of the electrical magnitudes. It avoids to apply any optimization technique, thus requiring a low computational burden. The method is first validated experimentally by comparing the identified dq current space vector against the acquired one using a resolver associated with a commercial drive. Finally, it is further validated by using a second PMSM associated with a sensorless drive, comparing the identified dq inductances with ground truth data obtained by a validated method.Peer ReviewedPostprint (author's final draft

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

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