Voltage Sag Causes Recognition with Fusion of Sparse Auto-Encoder and Attention Unet

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.

Automated summary

This paper proposes a voltage sag identification method that combines sparse auto-encoder and Attention Unet, achieving high accuracy in recognition by performing feature learning and extraction on high-dimensional data. It is of great significance for auxiliary decision-making in power quality management and governance.