Abstract

Aiming to solve the problems of multiple internal power components, high fault probability, high similarity of the fault features of different power components, difficulty of traditional fault diagnosis feature extraction and low accuracy of fault identification in high-voltage multilevel cascaded H-bridge inverters, this paper presents a fault diagnosis method based on an attention-parallel multisource data fusion residual network. First, a parallel residual neural network model is established, and the extracted multilevel three-phase voltage before filtering and the three-phase current waveform after filtering are converted into two-dimensional image data using a wavelet transform. Subsequently, a feature fusion module is integrated into the network structure to adaptively extract features at different network levels. This module locates key features using the attention mechanism. Then, it fuses useful fault information into feature images using the feature fusion mechanism, enhancing the feature representation capability of the network. Finally, the fault features extracted by the feature fusion module undergo the complete convolution operation. The final enhanced features are used as classification features and classified using a softmax layer. Experimental results demonstrate that the proposed method exhibits high fault diagnosis accuracy and adaptability.

中文翻译：

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基于注意力并行多源数据融合残差网络的级联H桥逆变器开路故障诊断

摘要

针对高压多电平级联H桥逆变器内部功率元件较多、故障概率高、不同功率元件故障特征相似度高、传统故障诊断特征提取困难、故障识别准确率低的问题，提出一种基于注意力并行多源数据融合残差网络的故障诊断方法。首先建立并行残差神经网络模型，利用小波变换将提取的滤波前的多电平三相电压和滤波后的三相电流波形转换为二维图像数据。随后，将特征融合模块集成到网络结构中，以自适应地提取不同网络级别的特征。该模块使用注意力机制来定位关键特征。然后，利用特征融合机制将有用的故障信息融合到特征图像中，增强网络的特征表示能力。最后，特征融合模块提取的故障特征经过完整的卷积运算。最终的增强特征用作分类特征并使用softmax层进行分类。实验结果表明，该方法具有较高的故障诊断精度和适应性。