A new wavelet based diagnosis and protection of faults in induction motor drives

In this paper, a novel diagnostic and protection algorithm based on wavelet neural network is developed and implemented in real-time for inverter faults in the vector controlled induction motor drives. The phase currents of an induction motor drive for different faulted and unfaulted conditions are preprocessed by wavelet packet transform in order to minimize the structure and timing of the proposed diagnostic technique. The wavelet packet transformed coefficients of line currents are used as inputs of a three-layer wavelet neural network. The performance of the proposed diagnosis and protection scheme is evaluated by simulation and experimental results. The proposed technique is evaluated and tested on-line for a laboratory 1-hp induction motor drive using the ds1102 digital signal processor 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.