scholarly journals Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

2000 ◽  
Vol 15 (2) ◽  
pp. 144-149 ◽  
Author(s):  
Xiang Fei ◽  
Xiaoyan He ◽  
Junzhou Luo ◽  
Jieyi Wu ◽  
Guanqun Gu

2013 ◽  
Vol 300-301 ◽  
pp. 1405-1411 ◽  
Author(s):  
Long Sheng Wang ◽  
Hong Ze Xu ◽  
Heng Yu Luo

An intelligent control strategy is proposed in this paper, which is applied to the high-speed train ATO (Automatic Train Operation) system in the cruise condition. The dynamics of a high-speed train is discussed based on a typical single-point-mass model and the force analysis in cruise state is studied. A fuzzy neural network control algorithm is incorporated into the ATO system aiming at improving the velocity and position tracking performance in the cruise operation of high-speed train. This control scheme adjusts the parameters of membership functions on-line and does not rely on the precise system parameters such as resistance coefficients which are very difficult to measure in practice. The numerical simulation verifies the effectiveness of this fuzzy neural network algorithm.


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