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Cascaded h-bridge multilevel inverters (CHBMLI) represent a promising solution for high-power applications due to their ability to produce high-quality output voltage with reduced harmonic distortion. This reduced THD makes the Cascaded MLIs useful for EV applications. CHBMLIs are more suitable for PV-based EV Charging applications due to their structure. However, like any complex electrical system, CHBMLIs are susceptible to faults, which can lead to system failures if not promptly detected and addressed. In this research, we propose an automated fault detection method for identifying open circuit (OC) and source faults in CHBMLIs using artificial neural networks (ANN). The ANN is trained using a dataset comprising voltage and current measurements under normal operating conditions and various fault scenarios. Once trained, the ANN is capable of accurately detecting OC faults in real-time, thus enabling proactive maintenance and preventing potential system downtime. Simulation results using MATLAB/Simulink demonstrate the effectiveness and reliability of the proposed fault detection approach, highlighting its potential for enhancing the reliability and performance of CHBMLI systems in practical applications.

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Automated Open Circuit Fault Detection in Cascaded H-Bridge Multilevel Inverters Using Artificial Intelligence Technique for EV Application

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  • 发表日期：

    2025年

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  • N. Kalaiarasi,
  • A. Sivapriya,
  • Pradeep Vishnuram,
  • Mohit Bajaj,
  • Arvind R. Singh

摘要

Cascaded h-bridge multilevel inverters (CHBMLI) represent a promising solution for high-power applications due to their ability to produce high-quality output voltage with reduced harmonic distortion. This reduced THD makes the Cascaded MLIs useful for EV applications. CHBMLIs are more suitable for PV-based EV Charging applications due to their structure. However, like any complex electrical system, CHBMLIs are susceptible to faults, which can lead to system failures if not promptly detected and addressed. In this research, we propose an automated fault detection method for identifying open circuit (OC) and source faults in CHBMLIs using artificial neural networks (ANN). The ANN is trained using a dataset comprising voltage and current measurements under normal operating conditions and various fault scenarios. Once trained, the ANN is capable of accurately detecting OC faults in real-time, thus enabling proactive maintenance and preventing potential system downtime. Simulation results using MATLAB/Simulink demonstrate the effectiveness and reliability of the proposed fault detection approach, highlighting its potential for enhancing the reliability and performance of CHBMLI systems in practical applications.