Nonintrusive Parameter Identification of IoT-Embedded Isotropic PMSM Drives

This article proposes a nonintrusive parameter identification procedure suitable for Internet-of-Things (IoT)-embedded isotropic permanent magnet synchronous machines (PMSMs). The method is designed for scenarios where only measurements collected without additional sensors, dedicated tests, or signal injection from in- service off-the-shelves motor drives are available. After automatic detection of the steady-state operating conditions (OCs) defined by the triplet current–speed–temperature, the rotor flux linkage, the stator resistance, the inductance, and the inverter distorted voltage term are estimated using two operating points. Particular emphasis is placed in defining the criteria of selecting these two optimal OCs to minimize the estimation errors. The latter are due to the inevitable difference between the parameters in different operating points. As a vessel to investigate the effectiveness of the proposed parameter identification, experimental and simulation tests carried out on a high-speed PMSM drive have been used for validation purpose. The proposed method is also compared with the existing methods from the literature to demonstrate its superiority in the considered scenario