The ill-convergence of multiparameter estimation due to the rank-deficient state equations of permanent-magnet synchronous machines (PMSMs) is investigated. It is verified that the PMSM model for multiparameter estimation under id = 0 control is rank deficient for simultaneously estimating winding resistance, rotor flux linkage, and winding inductance and cannot ensure them to converge to the correct parameter values. A new method is proposed based on injecting a short pulse of negative id current and simultaneously solving two sets of simplified PMSM state equations corresponding to id = 0 and id ≠ 0 by using an Adaline neural network. The convergence of solutions is ensured, while the minimum |id| is determined from the error analysis for nonsalient-pole PMSMs. The proposed method does not need the nominal value of any parameter and only needs to sample the winding terminal currents and voltages, and the rotor speed for simultaneously estimating the dq-axis inductances, the winding resistance, and the rotor flux linkage in nonsalient-pole PMSMs. Compared with existing methods, the proposed method can eliminate the estimation error caused by the variation of rotor flux linkage and inductance as a result of state change due to the injected d-axis current in the surface-mounted PMSM. The method is verified by experiments, and the results show that the proposed method has negligible influence on output torque and rotor speed and has good performance in tracking the variation of PMSM parameters due to temperature variation.

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Online Multiparameter Estimation of Nonsalient-Pole PM Synchronous Machines With Temperature Variation Tracking

To develop a high-performance and reliable permanent-magnet synchronous machine (PMSM) drive for electric vehicle (EV) applications, accurate knowledge of the PMSM parameters is of significance. This paper investigates online estimation of PMSM parameters and voltage source inverter (VSI) nonlinearity using current injection method in which magnetic saturation is also considered. First, a novel dc component-based current injection model considering VSI nonlinearity is proposed, which employs the dc components of dq -axis currents and voltages for PMSM parameter and VSI-distorted voltage estimation. This method can eliminate the influence of rotor position error on VSI nonlinearity estimation. Second, a simplified linear equation is employed to model the cross- and self-saturation of the dq -axis inductances during current injection, which can facilitate the estimation of the inductance variations induced by magnetic saturation. Third, a novel current compensation strategy is proposed to minimize the torque ripples caused by current injection, which contributes to making our approach applicable to both surface and interior PMSMs. Therefore, the proposed online parameter estimation approach can estimate the winding resistance, rotor flux, VSI-distorted voltage, and the varying dq -axis inductances under different operating conditions. The proposed approach is experimentally validated on a down-scaled laboratory interior PMSM prototyped for direct-drive EV powertrain.

Current Injection-Based Online Parameter and VSI Nonlinearity Estimation for PMSM Drives Using Current and Voltage DC Components

In high-performance control of ac machines through variable frequency drives, the knowledge of machine parameters plays a decisive role. The accuracy with which machine parameters can be known is directly related to the time and effort put during the testing and commissioning process. In the ever-demanding industrial environments, the time spent on parameter identification translates into loss of production. To reduce commissioning times, research in this direction has focused on automatizing the identification procedure without loss of accuracy. This paper reviews different lines of research adopted over the past few decades for machine parameter identification. Parameter estimation of ac machines is considered because of their widespread applications from servomechanisms to traction to aviation. The surveyed works include self-commissioning schemes that have become an integral part and a salient feature of modern electric drives. This feature enables the drives to automatically identify machine parameters and tune the control loops.

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Parameter Identification and Self-Commissioning in AC Motor Drives: A Technology Status Review

In this paper, a simple and robust method for parameter estimation at rotor standstill is presented for interior permanent magnet (IPM) synchronous machines. The estimated parameters are the stator resistance through dc test, the dq inductances using high-frequency injection, and the permanent magnet flux by means of a closed-loop speed control maintaining rotor stationary. The proposed method does not require either locking the rotor or additional/special power supplies. The validity of the suggested method has been verified by implementation on two IPM motor prototypes. Finally, the estimated parameters have been compared against results obtained through finite-element simulations and with machine magnetic characterization, separately performed, to validate the method's effectiveness. Saturation and cross-saturation effects are taken care of through amplitude modulation and cross-axis current application, respectively.

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Self-Commissioning of Interior Permanent- Magnet Synchronous Motor Drives With High-Frequency Current Injection

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.

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Online Parameter Estimation for Permanent Magnet Synchronous Machines: An Overview

In advanced motor control system, an accurate knowledge of motor parameters is essential in order to achieve a good performance. Some of these parameters, such as stator resistance are given by constructors, but they vary in different operating conditions. The purpose of this paper is to present an efficient dynamical identification method to estimate parameters of a permanent magnet synchronous machine (PMSM). Using this method, we can determine these parameters during different operating conditions. This method has been simulated and implemented in an experimental test bench using a dSpace board and obtained results proved the validity and efficiency of proposed method. Physical parameters estimation of a dynamical 3-parameter model thanks to dynamical measurements is performed using an output error algorithm based on non linear optimisation. Simulations results show that, if PMSM is fed by a PWM inverter, no extra-signal, such as a pseudo random binary sequence (PRBS) adding on speed reference in vector control of PMSM, is necessary to excite the machine to assure identification algorithm convergence. However an extra-signal is a disturbance in respect to the machine running.

R. Kahoul

Papers

Parameters estimation of permanent magnet synchronous machine without adding extra-signal as input excitation