Open Switch Fault Diagnosis of Cascaded H-Bridge Multi-Level Inverter Using Deep Learning

Abstract

Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to generate fine quality output voltage with minimal harmonic distortion. However, their intricate architecture presents notable challenges for fault diagnosis, particularly concerning open switch faults. In this study, we offer a deep learning-based method for diagnosing open switch faults in CHB-5LIs. We present a simulation model of the CHB-5LI with open switch faults and generate a dataset comprising voltage waveforms for various fault scenarios. Leveraging this dataset, we train a Convolutional-1D Neural Network (CNN-1D) featuring a multi-layer architecture comprising convolutional and fully connected layers, culminating in the Softmax function for classification. Our method achieves an impressive classification accuracy exceeding 98 percent on previously unseen fault scenarios, underscoring the efficiency of our approach for CHB-5LI fault diagnosis. Additionally, we conducted a thorough analysis of CNN-1D performance and compared it with traditional and other deep learning models for fault diagnosis techniques. The accuracy of other deep learning models on the generated dataset is as follows: RNN is 88.9 percent, 1D-ResNet is 88.8 percent, and Time Inception model is 89.4 percent. Simulation results showcase that our proposed CNN-1D based approach surpasses other methods in terms of accuracy and robustness, elucidating the potential of deep learning for fault diagnosis in intricate power electronics systems. The fault diagnosis time for the proposed method as a fault finding tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet. In conclusion, the recommended open switch fault analysis method using deep learning is an effective and efficient way to diagnose faults in cascaded H-bridge 5-level inverters. The recommended method can significantly improve system reliability and prevent catastrophic failures. Further research can explore the applicability of the projected method to identify faults in other power electronic systems.