  A fault diagnosis method for power electronics converters based on deep
feedforward network and wavelet compression is proposed in this paper. The
transient historical data after wavelet compression are used to realize the
training of fault diagnosis classifier. Firstly, the correlation analysis of
the voltage or current data running in various fault states is performed to
remove the redundant features and the sampling point. Secondly, the wavelet
transform is used to remove the redundant data of the features, and then the
training sample data is greatly compressed. The deep feedforward network is
trained by the low frequency component of the features, while the training
speed is greatly accelerated. The average accuracy of fault diagnosis
classifier can reach over 97%. Finally, the fault diagnosis classifier is
tested, and final diagnosis result is determined by multiple-groups transient
data, by which the reliability of diagnosis results is improved. The
experimental result proves that the classifier has strong generalization
ability and can accurately locate the open-circuit faults in IGBTs.
