Yapay Zeka Hızlı Bakış & Çıkarımlar
AI InsightExperimental and simulated results prove the usefulness and feasibility of the proposed strategy as compared with conventional methods.
📌 3 Maddede Bu Makale
Özet
Torque control of induction motors (IM) requires accurate estimation of the flux in the motor. But the flux estimate, when estimated from the stator circuit variables, is highly dependent on the stator resistance of the IM. As a result, the flux estimate is prone to errors due to variation in the stator resistance, especially at low stator frequencies. In this paper, an Artificial Neural Network (ANN) is used to adjust the stator resistance of an IM. A back propagation training algorithm was used in training the neural network for the simulation. The proposed ANN resistance estimator has shown good performance in both the transient and steady states. The system is first simulated with computer software and tested by hardware in the loop. Then, it is implemented using a TMS320C6711, 32-bit fixed point Digital Signal Processor (DSP). Experimental and simulated results prove the usefulness and feasibility of the proposed strategy as compared with conventional methods.
Abstract
Torque control of induction motors (IM) requires accurate estimation of the flux in the motor. But the flux estimate, when estimated from the stator circuit variables, is highly dependent on the stator resistance of the IM. As a result, the flux estimate is prone to errors due to variation in the stator resistance, especially at low stator frequencies. In this paper, an Artificial Neural Network (ANN) is used to adjust the stator resistance of an IM. A back propagation training algorithm was used in training the neural network for the simulation. The proposed ANN resistance estimator has shown good performance in both the transient and steady states. The system is first simulated with computer software and tested by hardware in the loop. Then, it is implemented using a TMS320C6711, 32-bit fixed point Digital Signal Processor (DSP). Experimental and simulated results prove the usefulness and feasibility of the proposed strategy as compared with conventional methods.