Stand-Still Self-Identification of Flux Characteristics for Synchronous Reluctance Machines Using Novel Saturation Approximating Function and Multiple Linear Regression

Motor characterization has a fundamental role in dynamics, torque accuracy, and efficiency of vector controlled Synchronous Reluctance Machine (SynRM) drives. Control performances and robustness in the whole speed/torque range, including the flux-weakening region, and in sensorless operation strongly rely on the knowledge of machine flux versus current characteristics. A convenient flux saturation approximating function is proposed in this paper, together with an efficient parameters self-identification procedure. The adopted strategy is very simple and can be performed at stand-still by injecting a proper voltage stimulus (current control is not involved), and does not require any additional hardware (motor can be either connected or disconnected from mechanical load). Nevertheless, an excellent fitting for the flux curves on both axes is obtained, using reasonable memory and computational resources. These features make the technique very suitable to motor self-identification in industrial drives. Experimental results based on a commercial drive and two SynRMs are reported to demonstrate the effectiveness of the proposal. Extensions of the method to the evaluation of the whole flux map (including cross-saturation effects) or to interior permanent-magnet machines is also investigated and verified.