Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

Review of Inductance Identification Methods Considering Inverter Nonlinearity for PMSM

Permanent magnet synchronous motors(PMSMs)are widely used in high-power-density and flexible control methods.Generally,the inductance changes significantly in real-time machine operations because of magnetic saturation and coupling effects.Therefore,the identification of inductance is crucial for PMSM control.Existing inductance identification methods are primarily based on the voltage source inverter(VSI),making inverter nonlinearity one of the main error sources in inductance identification.To improve the accuracy of inductance identification,it is necessary to compensate for the inverter nonlinearity effect.In this study,an overview of the PMSM inductance identification and the related inverter nonlinearity self-learning methods are presented.

Accurate Online MTPA Control of IPMSM Considering Derivative Terms

The conventional maximum torque per ampere(MTPA)operation usually neglects the derivative terms of interior permanent magnet synchronous motor(IPMSM)parameters,which significantly influences MTPA control accuracy.In this study,an MTPA control scheme that considers the derivative terms is developed,and a parameter identification strategy that considers the inverter to be non-ideal is developed for the calculation of the IPMSM parameters and derivative terms.In addition,the estimation accuracy of the motor parameters is further improved through the calibration of the nonlinear factors of the inverter.Finally,the effectiveness and accuracy of the proposed method is verified by simulation.This paper proposes practical methods for both inverter parameter estimation and accurate online MTPA control.