基于 EMD-CNN 的光伏逆变器开路故障诊断

绝缘栅双极型晶体管（IGBT）是光伏逆变器的核心部件，其若发生开路故障，不但会影响光 伏逆变系统的稳定运行，且可能会损坏系统设备。从减少传感器使用数量和融合多尺度特征的角度出发，对 开路故障诊断问题进行了研究，提出了一种基于经验模式分解（EMD）和二维卷积神经网络（2D-CNN）的 光伏逆变器故障诊断新方法。该方法利用 EMD 提取电流信号的本征模函数分量和原始信号组成二维特征数 据，然后将该数据输入 2D-CNN 模型中进行训练，最后实现 IGBT 开路故障的诊断。实验结果表明，该方法 不仅能提高故障诊断的准确率，而且在噪声环境下具备有效性和鲁棒性。

Open-Circuit Fault Diagnosis of Photovoltaic Inverters Based on EMD-CNN

As the core component of the photovoltaic inverter, insulated gate bipolar transistor (IGBT) will not only affect the stable operation of photovoltaic inverter system in case of an open circuit fault occurrence, but also damage the system equipment as well. In view of a reduction of the number of sensors and fuse multi-scale features, a new fault diagnosis method has thus been proposed for the photovoltaic inverter based on empirical mode decomposition (EMD) and two-dimensional convolution neural network (2D-CNN). The proposed method uses EMD to extract the intrinsic mode function component of current signal and the original signal so as to form two-dimensional feature data, with the data subsequently input into the 2D-CNN model for training, thus finally realizing the open circuit fault diagnosis of IGBT. Experimental results show that this method helps to improve the accuracy of fault diagnosis, characterized with a better performance of both effectiveness and robustness in a noisy environment.