Wavelet Neural Network-Based Diagnosis and Protection of Inverter Faults in Induction Motor Drives
نویسندگان
چکیده
In this paper, a wavelet neural network (WNN) based diagnostic algorithm is developed and implemented in real-time for the identification and detection of inverter faults in the vector controlled induction motor drives. The phase currents of an induction motor (IM) drive of different faulted and unfaulted conditions are preprocessed by the wavelet packet transform (WPT) algorithm in order to minimize the structure and timing of the proposed diagnostic technique using the WNN algorithm. The WPT coefficients are used as the inputs of a three-layer WNN. The performance of the proposed diagnosis scheme is evaluated by simulation and experimental results. The proposed technique is evaluated and tested on-line for a laboratory 1-hp IM motor drive using the ds1102 digital signal processor (DSP) board. In all the tests carried out, the type of fault is identified promptly and properly, and the tripping action is initiated almost at the instant or within one cycle of the fault occurrence.
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