This article proposes a precise identification scheme of an interior permanent-magnet synchronous motor (IPMSM) flux linkage through experimental tests considering motor nonlinearities, such as the magnetic saturation and spatial harmonics. In the proposed scheme, the flux linkage is identified based on the voltage equation of IPMSM. The fundamental and harmonic electromotive force in the rotor reference frame is obtained in real time by applying a discrete Fourier transform with the phase compensation of the time delay due to digital control and pulsewidth modulation. The effect of the inverter nonlinearity is compensated using an experimentally obtained lookup table. Based on the identified flux-linkage map, a flux-based IPMSM model that accurately simulates motor behavior can be constructed. The effectiveness of the proposed flux-linkage identification scheme is verified through the comparison of simulation and experimental results.

Identification of IPMSM Flux-Linkage Map for High-Accuracy Simulation of IPMSM Drives

During 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.

PMSM Parameter Estimation for Sensorless FOC Based on Differential Power Factor

In the voltage source inverter applications, inverter nonlinearities would affect the parameter identification process in many ways. Hence, this article proposes an offline identification method for resistance and dq -axis inductance surface by considering the inverter nonlinearity characteristics. A variable amplitude square-wave injection (VASI) scheme is proposed for the dq -axis inductance identification. The VASI method achieves the inductance identification with a novel data sampling strategy. Meanwhile, it can also establish the inductance surfaces by only a few identified data points with a polynomial fitting algorithm, which greatly reduces the identification time compared with the existing methods. The resistance identification is realized by a slope signal injection method, in which the effect of IGBT voltage drop is analyzed. In order to improve the identification accuracy, the inverter nonlinearities are compensated by a self-learning method considering the zero-axis voltage at different rotor positions. At the same time, the sampling error in zero current zones of abc-phases is researched. In order to verify the effectiveness and generality, the proposed method is carried out on two different test machines and confirmed by finite element analysis.

An Offline Parameter Self-Learning Method Considering Inverter Nonlinearity With Zero-Axis Voltage

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position offset error (RPOE) caused by the cross-saturation effect and other factors in high-frequency signal injection based methods, a current vector angle adaptive adjustment based online compensation method is proposed. This method can accurately detect and compensate the RPOE online only using two current vectors and has low sensitivity to the motor parameters, which improves the robustness of the system. Additionally, the two current vector angles can be adaptively adjusted according to the load conditions to improve the signal-to-noise ratio and reduce the current amplitude fluctuation during the RPOE detection process. Since neither offline measurement nor finite-element analysis is required, this method has superior adaptability and universality for applications. The effectiveness and feasibility are verified by experiments on a 2.2-kW interior PMSM drive platform.

Current Vector Angle Adaptive Adjustment Based Rotor Position Offset Error Suppression Method for Sensorless PMSM Drives

This article proposes an improved implicit model predictive current control (IMPCC) method for permanent magnet synchronous motor (PMSM) drives. Compared to the conventional implicit MPC, the proposed IMPCC has a much lower computational burden and thus is suitable for real-time applications. Two-step optimization is employed in this method to minimize the cost function based on continuous control set (CCS) while respecting the constraints. Furthermore, the proposed IMPCC uses an incremental model that eliminates the permanent magnet flux linkage. Its strong parameter robustness against stator resistance and inductance variations is also theoretically analyzed. Experimental results are presented to show the superior performance of the proposed IMPCC.

An Improved Implicit Model Predictive Current Control With Continuous Control Set for PMSM Drives

Existing online motor parameter identification methods mostly depend on the fundamental frequency voltage equations, which lead to the unsatisfactory identification effect at low current and low speed operation. This article proposes a parameter identification method based on the high frequency (HF) equivalent impedance model of permanent magnet synchronous motor with HF signal injection at both the dq -axes. This method identifies the resistance and the dq -axis inductances offline and online, along with the flux linkage online. In order to improve the identification accuracy, the parameter sensitivity analysis-based algorithm is proposed to detect the resistance and the flux linkage. Meanwhile, the inverter nonlinearities and the HF influence on parameter identification are compensated effectively. In order not to affect the normal operation of the motor drive, the selection of the amplitude, and the frequency of the injected signal is investigated. The proposed method is validated on a 2.2-kW motor and confirmed by finite element analysis. The experimental results show the good identification effect in different operation conditions.

Qingwen Cui

Papers

An Impedance Model-Based Multiparameter Identification Method of PMSM for Both Offline and Online Conditions