A Prediction Error Method-Based Self-Commissioning Scheme for Parameter Identification of Induction Motors in Sensorless Drives

To avoid inconvenience caused by shaft rotation and improve immunity to measurement noises, an elaborately designed scheme is proposed for parameter identification of induction motors. After analyzing the responses of a simple step-voltage test, a sequence of pseudorandom signals, customized to excite abundant dynamics in the featured frequency band of the motor, are injected into the stator in a single-phase mode at standstill. The crucial feature of the proposed scheme is that a nonlinear procedure is introduced to minimize “predicted errors” of the estimation model, which lowers influences of measurement noises notably, and thus the design of low-pass filters is simplified greatly. Experimental comparisons are carried out, including not only tests on a squirrel-cage motor, but also extended tests on a wound-rotor motor to testify accuracy of rotor-side parameters, both using a real inverter. The results indicate that the proposed scheme is able to estimate parameters required by controllers accurately in the noisy environment, and improve performances of sensorless motor drivers.