DSP Application on Torque Meter For Induction Motors

F.V. de Carvalho, L.E.B. da Silva, J.O.P. Pinto, and G.L. Torres (Brazil)


DSP application, torque estimation, Kalman filter, programmable cascade low-pass filter, recurrent neuralnetwork


This work describes the steps to implement a torque meter for three phase induction motors based on measurement of stator voltages and currents. The strategy uses the stator flux synthesis through Programmable Cascaded Low-Pass Filters (PCLPF). The electromagnetic torque estimation is done by a DSP microprocessor in real time. The PCLPF filter outlines the problem of necessary numeric integration to calculate the stator flux. The Programmable Cascaded Low-Pass Filter is implemented using Recurrent Neural Network trained by Kalman filter. The DSP based implementation of a torque meter has the same precision comparing with torque meters based on torsion of metallic axes with known elastic constant and strain gauges.

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