Abstract: A motor control system includes a motor including a plurality of windings, a first sensor configured to sense a first operating parameter of the motor, a second sensor configured to sense a second operating parameter of the motor, and memory hardware configured to store a machine learning model and computer-executable instructions. The machine learning model is trained to generate a winding temperature estimation output based on motor operating parameter inputs. The motor control system includes processor hardware configured to execute the instructions and use the machine learning model to cause the motor control system to generate a winding temperature estimation output using the machine learning model based on the first operating parameter and the second operating parameter, the temperature estimation output indicative of a predicted temperature of the plurality of windings, and control the motor based on the winding temperature estimation output.

Abstract: A motor control system includes a motor including a plurality of windings, a first sensor configured to sense a first operating parameter of the motor, a second sensor configured to sense a second operating parameter of the motor, and memory hardware configured to store a machine learning model and computer-executable instructions. The machine learning model is trained to generate a winding temperature estimation output based on motor operating parameter inputs. The motor control system includes processor hardware configured to execute the instructions and use the machine learning model to cause the motor control system to generate a winding temperature estimation output using the machine learning model based on the first operating parameter and the second operating parameter, the temperature estimation output indicative of a predicted temperature of the plurality of windings, and control the motor based on the winding temperature estimation output.

Abstract: A motor control system includes a motor including a plurality of windings, a first sensor configured to sense a first operating parameter of the motor, a second sensor configured to sense a second operating parameter of the motor, and memory hardware configured to store a machine learning model and computer-executable instructions. The machine learning model is trained to generate a winding temperature estimation output based on motor operating parameter inputs. The motor control system includes processor hardware configured to execute the instructions and use the machine learning model to cause the motor control system to generate a winding temperature estimation output using the machine learning model based on the first operating parameter and the second operating parameter, the temperature estimation output indicative of a predicted temperature of the plurality of windings, and control the motor based on the winding temperature estimation output.

Abstract: In an example embodiment, a controller includes a memory having computer-readable instructions stored therein and a processor. The processor is configured to execute the computer-readable instructions to determine a first set of inductance values for at least one load machine and second set of inductance values for a generator machine, determine a first set of gains for the at least one load machine based on the first set of inductance values and a regulator bandwidth, determine a second set of gains for the generator machine based on the second set of inductance values and the regulator bandwidth, and generate voltage commands for driving the at least one load machine and the generator machine based on at least the first and second sets of gains.

Abstract: In an example embodiment, a controller includes a memory having computer-readable instructions stored therein and a processor. The processor is configured to execute the computer-readable instructions to determine a first set of inductance values for at least one load machine and second set of inductance values for a generator machine, determine a first set of gains for the at least one load machine based on the first set of inductance values and a regulator bandwidth, determine a second set of gains for the generator machine based on the second set of inductance values and the regulator bandwidth, and generate voltage commands for driving the at least one load machine and the generator machine based on at least the first and second sets of gains.

Abstract: A current measurement module is adapted to measure an observed current level for the inverter. A data processor or mode controller accesses a stored representation of first current level versus rotor speed output at which a total harmonic distortion level is less than threshold total harmonic distortion level. The data processor or mode controller enables the inverter to use SVPWM (space vector pulse width modulation) if the observed current level is less than the first current level for the measured or determined rotor speed. Further, the data processor or mode controller enables the inverter to use DPWM (discontinuous pulse width modulation) if the observed current level is greater than or equal to the first current level for the measured or determined rotor speed.

Abstract: A current measurement module is adapted to measure an observed current level for the inverter. A data processor or mode controller accesses a stored representation of first current level versus rotor speed output at which a total harmonic distortion level is less than threshold total harmonic distortion level. The data processor or mode controller enables the inverter to use SVPWM (space vector pulse width modulation) if the observed current level is less than the first current level for the measured or determined rotor speed. Further, the data processor or mode controller enables the inverter to use DPWM (discontinuous pulse width modulation) if the observed current level is greater than or equal to the first current level for the measured or determined rotor speed.

Abstract: A current measurement module is adapted to measure an observed current level for the inverter. A data processor or mode controller accesses a stored representation of first current level versus rotor speed output at which a total harmonic distortion level is less than threshold total harmonic distortion level. The data processor or mode controller enables the inverter to use SVPWM (space vector pulse width modulation) if the observed current level is less than the first current level for the measured or determined rotor speed. Further, the data processor or mode controller enables the inverter to use DPWM (discontinuous pulse width modulation) if the observed current level is greater than or equal to the first current level for the measured or determined rotor speed.

Abstract: A current measurement module is adapted to measure an observed current level for the inverter. A data processor or mode controller accesses a stored representation of first current level versus rotor speed output at which a total harmonic distortion level is less than threshold total harmonic distortion level. The data processor or mode controller enables the inverter to use SVPWM (space vector pulse width modulation) if the observed current level is less than the first current level for the measured or determined rotor speed. Further, the data processor or mode controller enables the inverter to use DPWM (discontinuous pulse width modulation) if the observed current level is greater than or equal to the first current level for the measured or determined rotor speed.