Stator Fault Diagnosis of a Permanent Magnet Synchronous Generator Using a Decision Tree Classifier

This work presents a decision tree-based classifier to diagnose short circuit faults between the stator’s turns in a permanent magnet synchronous generator. The generator currents are processed with the discrete wavelet transform to obtain features to feed the binary classifier that performs the diagnosis. Experimental results are presented to show the performance of the proposed scheme.