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<p>This paper proposes a fault diagnosis framework for multiple open-switch faults in four-level active neutral-point-clamped (ANPC) inverters under limited labeled data conditions. In multilevel inverter systems, the number of possible fault combinations increases exponentially with the number of switches. The proposed method leverages one-phase voltage signals with half-bridge (HB) reconstruction to improve observability and reduce data requirements. An adaptive convolutional neural network with reinforced attention optimization (ACNN-RAO) is developed, where depthwise convolutions minimize the parameters and a reinforcement agent dynamically adjusts attention weights to enhance classification accuracy. For online implementation, three parallel models are deployed to classify single, double, and triple open-switch faults. Experimental results demonstrate 99.6% diagnostic accuracy with 96% fewer labeled data, and fault identification within 1.5 fundamental cycles.</p>

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Multiple open-switch fault diagnosis in four-level ANPC inverters using adaptive CNN with reinforced attention

  • 期刊论文

  • 发表日期：

    2026年2月17日

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  • Dyan Puspita Apsari,
  • Dong-Choon Lee

摘要

This paper proposes a fault diagnosis framework for multiple open-switch faults in four-level active neutral-point-clamped (ANPC) inverters under limited labeled data conditions. In multilevel inverter systems, the number of possible fault combinations increases exponentially with the number of switches. The proposed method leverages one-phase voltage signals with half-bridge (HB) reconstruction to improve observability and reduce data requirements. An adaptive convolutional neural network with reinforced attention optimization (ACNN-RAO) is developed, where depthwise convolutions minimize the parameters and a reinforcement agent dynamically adjusts attention weights to enhance classification accuracy. For online implementation, three parallel models are deployed to classify single, double, and triple open-switch faults. Experimental results demonstrate 99.6% diagnostic accuracy with 96% fewer labeled data, and fault identification within 1.5 fundamental cycles.