Estimador do comportamento do conjugado de motores de indução através de DSP

Abstract

The purpose of this work is the implementation of a method for estimation of the dynamic torque to induction motor, using the three phases voltages and currents samples, with a DSP (Digital Signal Processor) based circuit tool. Nowadays, the vector control is used in drives where high-performance dynamic is required. The flux is measured directly in the gap, with Hall effect sensors, or another measurement technique. The gap measured flux feedback is made available to the drive system and the currents, which produce torque, are synthesized. The direct flux measurement is expensive and the process accuracy may not be enough. currents and then, the stator Another process consists in the measurement of the stator voltages and flux is calculated by means of equations processing. The process strategy is the stator flux estimation by the programmable cascaded low-pass filter (PCLPF). This method is well known because it permits the stator voltage integration, since extremely low frequency to high frequency on the field-weakening range, without introducing any offset at the output. Besides the stator voltage and current measurement, the only parameter under consideration to the stator flux estimation, using the programmable low- pass filter (PCLPF) method, is the stator equivalent impedance, where the significant component is the resistance. This stator flux estimation method eliminates flux and speed sensors, decreasing cost and making the system reliably better. It is important to notice that the programmable cascaded low-pass filter (PCLPF) was substituted by recurrent neural network (RNN), which permits the fast and simple flux estimation. The recurrent neural network (RNN) response, compared with another digital signal processing based PCLPF implementation, shows equal behavior at steady state, but is superior at transient response. A Kalman filter based algorithm is used to recurrent neural network (RNN) training.