Ensuring reliability and safety in multilevel power converter topologies requires effective fault diagnostics, especially considering the greater number of components involved compared to traditional converters. Recent research has recognized the potential of Artificial Neural Networks (ANN) in enhancing diagnostic strategies. In this context, this study proposes an innovative approach for detecting devices faults in multilevel converters using Convolutional Neural Networks (CNN) through 2D image-based classification. The simulation results demonstrate the efficacy of this method, enabling more dependable and efficient multilevel power electronic systems.
Fault Detection in Cascaded H-Bridge Inverters using Spectrogram Analysis and Convolutional Neural Networks
Rokocakau, Samuela;Tresca, Giulia;Zanchetta, Pericle;Frosini, Lucia
2023-01-01
Abstract
Ensuring reliability and safety in multilevel power converter topologies requires effective fault diagnostics, especially considering the greater number of components involved compared to traditional converters. Recent research has recognized the potential of Artificial Neural Networks (ANN) in enhancing diagnostic strategies. In this context, this study proposes an innovative approach for detecting devices faults in multilevel converters using Convolutional Neural Networks (CNN) through 2D image-based classification. The simulation results demonstrate the efficacy of this method, enabling more dependable and efficient multilevel power electronic systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
