Multilevel Inverter Fault Diagnosis Using Differentiable Architecture Search for Edge Deployment

作者：Haocheng Hu, Tianzhen Wang, Haoran Wang, Yassine Amirat · 发表于：AI · 年份：2026 · DOI：10.3390/ai7060208 · 研究领域：Multilevel Inverters and Converters、Silicon Carbide Semiconductor Technologies、HVDC Systems and Fault Protection

With the increasing penetration of renewable energy systems, multilevel inverters have been widely adopted to meet the growing demand for high-power and high-quality energy conversion. Among various multilevel topologies, cascaded H-bridge multilevel inverters (CHMIs) are particularly attractive due to their modular structure and improved output voltage quality. However, the increased number of power semiconductor devices and switching states significantly complicates fault diagnosis under practical operating conditions. Currently, most existing neural networks for fault diagnosis are manually designed based on domain expertise. This may limit their adaptability to task-specific fault patterns as well as edge-side inference performance. To reduce the dependence on manually designed diagnostic networks, an edge-oriented fault diagnosis framework based on differentiable architecture search (DARTS) is proposed to automatically design task-specific diagnostic networks. A simplified special cell search strategy is adopted to improve search efficiency and facilitate practical deployment. The searched architectures are lightweight and suitable for deployment on edge platforms. The experiments show that the proposed method achieves an average diagnostic accuracy of 99.44% on the test set under the RL load of (7Ω,6mH). Furthermore, the searched model contains only 0.2417 M trainable parameters, and edge deployment experiments on the Jetson Orin Nano platform show low-latency inference...