Fault Diagnosis of DAB Converters Based on ResNet With Adaptive Threshold Denoising

Dual-active-bridge (DAB) converter can connect several ac distribution networks with different voltage levels by a common dc bus to form ac/dc hybrid distribution networks. Its reliability issue has become a hot research topic in academia and industry. In this article, the fault operation mode of DAB is analyzed in detail, and a fault diagnosis strategy based on adaptive threshold denoising for residual networks is proposed on this basis. The main module of the diagnosis algorithm consists of an improved channel attention module and threshold algorithm. The improved channel attention module improves the network’s ability to learn features by using two ways to extract different features of the fault signal separately. The threshold algorithm is embedded into the deep network as a nonlinear layer. The thresholds can be learned adaptively in the established cells, which plays a good adaptive denoising capability. Finally, by building a hardware-in-the-loop system, we compare the signal denoising ability of various methods at different signal-to-noise ratios. The experimental results demonstrate that the proposed method can accurately locate the open-circuit fault and achieve 99.82% accuracy under the original data. It is also more accurate than other methods under different signal-to-noise ratios, which verifies the correctness and effectiveness of the method.