High-precision voltage sag cause identification is significant in solving the power quality problem. It is challenging for traditional deep learning models to balance training complexity and recognition performance when processing high-dimensional staging data samples, which affects the final recognition effect. This paper proposes a voltage sag identification method that fuses a sparse auto-encoder and Attention Unet. The model uses a sparse auto-encoder to perform unsupervised feature learning on the high-dimensional voltage sag waveform data and automatically obtains the deep low-dimensional features. Attention Unet, fused with cross-layer spatial and channel attention modules, further extracts these features to obtain recognition results with high performance. Compared with other deep learning recognition methods, the noise-adding experiments and the measured data are verified, indicating that the proposed method has low training complexity, higher recall, and better noise immunity. It benefits auxiliary decision-making for power quality management and governance.

中文翻译：

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电压暂降引起的识别与稀疏自动编码器和注意力Unet的融合

高精度电压暂降原因识别对于解决电能质量问题具有重要意义。传统深度学习模型在处理高维分期数据样本时难以平衡训练复杂度和识别性能，影响最终识别效果。本文提出了一种融合稀疏自动编码器和注意力 Unet 的电压暂降识别方法。该模型使用稀疏自编码器对高维电压暂降波形数据进行无监督特征学习，自动获取深层低维特征。Attention Unet 融合了跨层空间和通道注意模块，进一步提取这些特征以获得高性能的识别结果。与其他深度学习识别方法相比，通过加噪实验和实测数据验证，表明所提方法训练复杂度低、召回率高、抗噪性好。它有利于电能质量管理和治理的辅助决策。