Glocal identification methods for low-order lumped parameter thermal networks used in permanent magnet synchronous motors

High utilization of permanent magnet machines without monitoring their internal temperatures has negative impact on windings and permanent magnets. Lumped-parameter thermal networks (LPTNs) are therefore used to estimate magnet and winding temperatures. LPTNs identification is an intricate process as LPTNs can only be accurately described as linear-parameter varying systems (LPV). Thus specialized identification techniques are required such as global and local methods studied in the last decades. This paper studies the performance of the so-called glocal methods. Hence, SMILE, H2-norm, and H∞-norm methods are implemented and compared. All three glocal methods are able to represent the system with high accuracy. H2-norm and ∞-norm methods achieve slightly better accuracy than SMILE; however, complications such as computational burden and local minimum convergence favor SMILE. The latter has a faster convergence and can achieve high accuracy with maximum temperature estimations errors of 6.8 °C, 6.2 °C, and 4.7 °C for the winding, end-winding, and permanent magnets. Finally, it was found that the model accuracy does not improve majorly by increasing the number of local models. It was estimated that a segmentation of the operating range (speed and current) into 4 or 5 parts respectively is enough to obtain a relative accurate LPV.