Online Inductances Estimation of the Permanent Magnet Synchronous Machines based on Deep Learning and Recursive Least Square Algorithms

Tóm tắt

This paper presents a novel method to identify in real time d- and q- axes inductances of the permanent magnet synchronous machines (PMSMs), which normally vary during the operation due to the saturation of the magnetic fields. The proposed method is based on the combination of deep learning and recursive least square algorithms. The deep learning model is trained offline to compensate the non-linearity effect of the voltage source inverter and the position measurement error, while the recursive least square algorithm is employed to estimate online d- and q- axes inductances based on the compensation d- and q- axes stator voltages, measured d- and q- axes stator currents and the operating speed. The proposed methods can overcome the problems associated with the existing model-based methods known as the effect of position measurement error and the unavailability of accurate information of the inverter. Extensive experimental studies were conducted to evaluate the estimation accuracy and the robustness of the proposed method in critical operating conditions including the variation of load torque, operating speed, and field-weakening condition.