Automatic Identification of Machine Parameters for Motor Drives

In industrial settings, a common challenge associated with electrical machines is the lack of parameters, which are not always available from the machine manufacturer. These parameters play a crucial role in tuning the control gains of Field-Oriented Control (FOC). Traditional parameter identification methods, widely accepted in the literature, are the standard IEEE tests such as DC measurement, no-load test, locked-rotor test, and short circuit test. However, their implementation can be impractical as they necessitate additional equipment that may be costly and not readily accessible. This thesis addresses self-commissioning procedures as a solution to this challenge, aiming to automatically identify electrical parameters of machine equivalent circuits. Specifically, this study focuses on the Imperix motor testbench, comprising Induction Machine (IM) and Surface Mounted Permanent Magnet Synchronous Machine (SPMSM). Self-commissioning is a standstill procedure that utilizes signal injection through a power converter and the available sensors, with minimal operator intervention and no additional equipment. For the SPMSM, the parameters under study include the stator resistance, the synchronous inductance, and the Permanent Magnets (PM)-flux. The resistance is estimated through direct current injection considering the inverter non-linearity. The synchronous inductance and its saturation characteristic are examined using high-frequency sinusoidal injection with and without DC bias via a Current Controller (CC), and square wave voltage injection through hysteresis control. The PM-flux is determined by accelerating the SPMSM using the IM as a prime mover in an open circuit configuration. This deviation from the standstill constraint of the self-commissioning procedure is necessary as the PM’s effect becomes visible only when the rotor speed is non-zero. For the IM, the stator resistance, the leakage inductance, the rotor resistance, and the magnetizing inductance are analyzed. The stator resistance and inverter non-linearity are identified using the same method as for the SPMSM. The leakage inductance is tested with a high-frequency sinusoidal injection with stepped DC bias, with which the saturation characteristic is built. Then, a DC-biased low-frequency sinusoidal injection identifies the rotor resistance. The magnetizing inductance is not identified in this work because of the extensive nature of the problem and time constraints. Comparison with standard IEEE tests serves as validation, demonstrating close alignment of the results, except for discrepancies in the unsaturated SPMSM synchronous inductance estimation. The introduced innovation involves adapting existing procedures, initially developed for other AC machines, to SPMSM applications, for which only limited literature is available. Overall, this work makes a valuable contribution to understanding the influence of inverter non-linearity and saturation behavior on parameter identification. It also opens the door to integrating saturation effects into control algorithms, which enables dynamic adjustment of FOC gains, potentially enhancing control performance.