Safety Behavior Recognition for Substation Operations Based on a Dual-Path Spatiotemporal Network

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety judgment. However, existing behavior recognition methods have difficulty accurately identifying operational states in complex scenarios involving continuous actions, partial occlusions, and fine-grained manipulations. To address these challenges, this paper proposes a safety behavior recognition method for substation operations based on a dual-path spatiotemporal network. Personnel localization is achieved using YOLOv8, while behavior classification is performed through the SlowFast framework. In the Slow pathway, an ECA attention mechanism is integrated with residual structures to enhance the representation of sustained operational postures. In the Fast pathway, a multi-path excitation residual network is introduced to fuse temporal, channel, and motion information, improving the multi-scale representation of local action variations. Furthermore, to mitigate the issue of class imbalance in substation operation data, Focal Loss based on binary cross-entropy is incorporated to adaptively down-weight easily classified samples. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 87.77% and an F1-score of 85.56% across multiple operation scenarios. The results further indicate improved recognition stability and adaptability, supporting safe substation operation and maintenance in renewable energy-integrated power systems.