Journal of Advanced Materials in Engineering (Esteghlal)

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Stator flux oriented vector control of induction motor (IM) drives for speed sensorless control has several advantages. But the application of a pure integrator for the flux estimation is difficult due to the presence of measurement noise and dc offset. To overcome these problems, some have used a programmable cascaded low pass filter (PCLPF). In this paper, it is shown that some problems still exist and some new problems arise from this approach. In order to solve these problems, a novel compensation method is proposed. In this scheme, the dc offset is detected and subtracted from the estimated flux along d and q axes. The simulation results show that it works well in the low speed region as well as in the transient state. The oscillation of the torque and the estimated flux are also reduced notably when the torque reference changes rapidly.

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This paper presents a method based on neural networks to detect broken rotor bars and end rings in squirrel cage induction motors. In the first part, detection methods are reviewed and traditional methods of fault detection as well as dynamic model of induction motors are introduced using the winding function method. In this method, all stator and rotor bars are considered independently in order to check the performance of the motor for any faults in the parts. Then the frequency spectrum of current signals is derived using the Fourier transform and analyzed under various conditions. In the second part of the paper, an analytical discussion of the theoretical principles is presented to arrive at a simple algorithm for fault detection based on neural networks. The neural network has been trained using the information from a 1.1 KW induction motor. Finally, the system is tested with different values of load torque and is found capable of working on-line to detect all normal and ill-performing conditions.