Artificial neural network approach for locating internal faults in salient-pole synchronous generator

This paper presents an artificial neural network approach for locating internal faults in salient-pole synchronous generator. This method uses samples of magnetic flux linkages to reach a decision. Meyer wavelet probabilistic neural network (MWPNN) as main part of this fault diagnosis method is utilized to detect internal faults. The basic PNN is integrated with discrete wavelet transform (DWT) to construct the MWPNN. The MWPNN is trained by features extracted from the magnetic flux linkage data through the discrete wavelet transform (DWT). To implement this method practically, the training database for the PNN is generated by 3D-FEM. Since the FEM simulation is a time-consuming process, hence in order to reduce the number of FEM computations generalized regression neural network (GRNN) is used to estimate the flux linkages characteristics of synchronous generator under various conditions. The diagnosis performance of the proposed method is validated via experimental data, derived from a 4-pole, 380 V, 1500 rpm, 50 Hz, 50 KVA, 3-phase salient-pole synchronous generator.