TY - JOUR

T1 - Open switch fault diagnosis of cascaded H-bridge 5-level inverter using deep learning

AU - Arif, Muhammad Nouman

AU - Din, Zaki Ud

AU - ul Haq, Azhar

AU - Cheema, Khalid Mehmood

AU - Milyani, Ahmad H.

AU - Naeem-ul-Islam, null

AU - Ashfaq, Iqra

PY - 2024/5/22

Y1 - 2024/5/22

N2 - Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-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 propose a deep learning-based approach 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 efficacy 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 diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet.

AB - Cascaded H-bridge 5-level inverters (CHB-5LIs) have gained significant traction in high-power applications owing to their capacity to produce high-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 propose a deep learning-based approach 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 efficacy 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 diagnosis tool for the simulation case is 0.060 ms, compared to 0.062 ms for RNN and 0.065 ms for ResNet.

KW - cascaded H-bridge 5-level inverter

KW - fault detection

KW - deep learning

KW - open switch fault diagnosis

KW - convolutional neural network (CNN)

U2 - 10.3389/fenrg.2024.1388273

DO - 10.3389/fenrg.2024.1388273

M3 - Journal article

VL - 12

JO - Frontiers in Energy Research

JF - Frontiers in Energy Research

SN - 2296-598X

M1 - 1388273

ER -