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 lowpass 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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