针对传统电压源型静止同步补偿器中逆变器故障诊断存在的信号特征提取不充分，深度学习网络识别能力不足以及高噪声情况下识别率较低等问题，提出了一种基于双模式分解、多通道输入(MCI)、并行卷积神经网络(PCNN)、双向长短时记忆(BiLSTM)网络和自注意力(SA)机制组合的逆变器故障诊断方法。首先利用变分模态分解和时变滤波经验模态分解对逆变器输出的三相电流进行分解，降低原始信号复杂程度，实现不同模态分量间的规律互补；其次，利用MCI-PCNN-BiLSTM-SA组合模型对特征矩阵进行深层特征提取、学习和识别；最后，通过仿真进行验证，结果表明所提方法特征提取能力较强，在无噪声情况下平均识别率高达99.48%，在高噪声情况下平均识别率达95.59%。

Aiming at the problems of insufficient signal feature extraction, insufficient recognition ability of deep learning network and low recognition rate under high noise condition in inverter fault diagnosis in traditional voltage source converter static synchronous compensator, an inverter fault diagnosis method based on the combination of dual-mode decomposition, multi-channel input(MCI), parallel convolutional neural network(PCNN), bi-directional long and short-term memory (BiLSTM) neural network and self-attention(SA) mechanism is proposed. Firstly, the three-phase current output of the inverter is decomposed by variational mode decomposition and time-varying filter empirical mode decomposition, which reduces the complexity of the original signal and realizes the law complementation between different modal components. Secondly, MCI-PCNN-BiLSTM-SA combined model is used to extract, learn and recognize the feature matrix. Finally, the proposed method is validated by simulation, and the results show that the proposed method has strong feature extraction ability, with an average recognition rate of 99.48% in the case of no noise and 95.59% in the case of high noise.