Fault Diagnosis using 1-D Convolutional Transformer Hybrid Neural Network for Cascaded H-Bridge Converters

In this work, the 1-D Convolutional Transformer Hybrid Neural Network is compared to other deep learning and non-neural techniques for Open Switch Fault Detection and Localization in a Cascaded H-Bridge Converter. The novel approach outperforms others in multiclass classification switch fault detection and localization. Two H-Bridge topologies are analyzed, with the hybrid neural network achieving high prediction accuracy in both. Key metrics like prediction accuracy, speed and memory size highlight the architecture's advantages. The models detect overall system health, locate faulty phase modules and identify single switch faults, reaching accuracy rates of up to 99% and 98% for Topologies I and II, respectively. The model size is approximately 2MB for both topologies, with average prediction times of 1ms for Topology I. Topology II's 2-class scenario for overall health status has an average prediction time of 9ms, while other scenarios take 1.5ms. The advancement of AI-based technologies can help improve the reliability and robustness of inverter systems by understanding and implementing strategies discussed in this paper.