Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression

TL;DR

This paper presents a fault diagnosis method for power electronics converters using wavelet compression and a deep feedforward network, achieving over 97% accuracy and improved speed for reliable fault detection.

Contribution

It introduces a novel combination of wavelet-based data compression and deep neural networks for efficient and accurate fault diagnosis in power electronics.

Findings

Fault diagnosis accuracy exceeds 97%.

Method effectively locates open-circuit faults in IGBTs.

Wavelet compression accelerates training speed.

Abstract

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.