Neural speed filtering for sensorless induction motor drives

Effective sensorless dynamic speed estimation is desirable for both speed sensorless motor-drive applications and for on-line induction motor condition monitoring and assessment. In this paper, a sensorless neural adaptive speed filter is developed for induction motors running off a voltage source inverter. The filter is demonstrated by comparisons with experimental speed measurements and spectral speed estimates. In addition to nameplate information required for the initial set-up, the proposed neural speed filter uses only measured motor currents and voltages. Initial training of the speed filter is accomplished off-line, using rotor slot harmonic-based speed estimates. The developed speed filter is tested on data from a healthy motor and motor with 4 broken rotor bars. The resulting speed filtering accuracy compares favorably with speed estimation results reported in the literature. The study demonstrates the feasibility of neural adaptive speed filters for effective inverter-fed induction motor speed estimation, without explicit use of motor model parameters and speed measurements. (C) 2003 Elsevier Ltd. All rights reserved.