We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Intelligent diagnosis of cascaded H‐bridge multilevel inverter combining sparse representation and deep convolutional neural networks.
- Authors
Du, Bolun; He, Yigang; Zhang, Chaolong
- Abstract
Effective fault diagnosis for cascaded H‐bridge multilevel inverter (CHMLI) can reduce failure rate and prevent the unscheduled shutdown. Nevertheless, traditional signal‐based feature extraction and feature selection methods show poor distinguishability for insufficient fault features in a one‐dimensional space. The shallow learning models are prone to fall into local extremum, slow convergence speed and overfitting. To cope with these problems, a novel image‐oriented fault diagnosis strategy based on sparse representation (SR) and deep convolutional neural network (DCNN) is proposed for CHMLI. Initially, Hilbert–Huang transform (HHT) is applied to obtain the HHT spectral images of original monitoring signals, where these images comprehensively represent the features with detailed information of multiple domains on the time‐frequency plane. Furthermore, an image fusion method based on the SR algorithm is employed on these spectral images of the same fault category to construct fused feature images, which effectively reflects the complicated relationships between the measured signals and fault features. Ultimately, the DCNN models can not only mine the relationship between the various fault categories and the different fused feature images but also can alleviate the problem of overfitting that is caused by the limited availability of training samples.
- Subjects
ELECTRIC inverters; CONVOLUTIONAL neural networks; HILBERT-Huang transform; FEATURE extraction; ELECTRIC current converters
- Publication
IET Power Electronics (Wiley-Blackwell), 2021, Vol 14, Issue 6, p1121
- ISSN
1755-4535
- Publication type
Article
- DOI
10.1049/pel2.12094