Nonintrusive Parameter Identification of IoT-Embedded Isotropic PMSM Drives
Elia Brescia, Paolo Roberto Massenio, Mauro Di Nardo, Giuseppe Leonardo Cascella, Chris Gerada, Francesco Cupertino
Abstract
This paper proposes a nonintrusive parameter identification procedure suitable for Internet-of-Things (IoT)-embedded isotropic permanent magnet synchronous machines (PMSM). 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 the automatic detection of the steady state operating conditions 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 operating conditions 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 existing methods from the literature to demonstrate its superiority in the considered scenario.