Incremental Spatio-Temporal Augmented Sampling for Power Grid Operation Behavior Recognition

Accurate recognition of power grid operation behaviors is crucial for ensuring both safety and operational efficiency in smart grid systems. However, this task presents significant challenges due to dynamic environmental variations, limited labeled training data availability, and the necessity for continuous model adaptation. To overcome these limitations, we propose an Incremental Spatio-temporal Augmented Sampling (ISAS) method for power grid operation behavior recognition. Specifically, we design a spatio-temporal Feature-Enhancement Fusion Module (FEFM) which employs multi-scale spatio-temporal augmented fusion combined with a cross-scale aggregation mechanism, enabling robust feature learning that is resilient to environmental interference. Furthermore, we introduce a Selective Replay Mechanism (SRM) that implements a dual-criteria sample selection strategy based on error variability and feature-space divergence metrics, ensuring optimal memory bank updates that simultaneously maximize information gain while minimizing feature redundancy. Experimental results on the power grid behavior dataset demonstrate significant advantages of the proposed method in recognition robustness and knowledge retention compared to other methods. For example, it achieves an accuracy of 89.80% on sunny days and maintains exceptional continual learning stability with merely 2.74% forgetting rate on three meteorological scenarios.