Yan Yan, Wu Jiaqi, Cao Yanfei, Liu Bo, Li Chen, Shi Tingna
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Sensors (Basel). 2024 Mar 7;24(6):1745. doi: 10.3390/s24061745.
This study constructs a power switching device open-circuit fault diagnosis model for a three-level neutral point clamped inverter based on the multi-scale shuffled convolutional neural network (MSSCNN) and extracts and classifies the fault information contained in the output current of inverters. The model employs depthwise separable convolution and channel shuffle techniques to improve diagnostic accuracy and reduce model complexity. The experimental results show that the new model has lower model complexity, better noise resistance and higher average diagnostic accuracy compared with fault diagnosis models based on CNN, ResNet, ShuffleNet V2 and Mobilenet V3 networks.
本研究基于多尺度混洗卷积神经网络（MSSCNN）构建了三电平中点箝位逆变器的功率开关器件开路故障诊断模型，并提取和分类逆变器输出电流中包含的故障信息。该模型采用深度可分离卷积和通道混洗技术来提高诊断精度并降低模型复杂度。实验结果表明，与基于CNN、ResNet、ShuffleNet V2和Mobilenet V3网络的故障诊断模型相比，新模型具有更低的模型复杂度、更好的抗噪声能力和更高的平均诊断精度。
