Active Hard Sample Learning for Violation Action Recognition in Power Grid Operation - Lingwen Meng; Di He; Guobang Ban; Guanghui Xi; Anjun Li; Xinshan Zhu (Information)
Power grid operation occurs in complex, dynamic environments where the timely identification of operator violations is essential for safety. Traditional methods often rely on manual supervision and rule-based detection, leading to inefficiencies. Existing deep learning approaches, while powerful, require fully labeled data and long training times, thereby increasing costs. To address these challenges, we propose an active hard sample learning method specifically for the violation action recognition of operators in power grid operation. We design a hard instance sampling module with multi-strategy fusion based on active learning to improve training efficiency. This module identifies hard samples based on the consistency of models or samples, where we develop uncertainty evaluation and the instance discrimination strategy to assess the contributions of samples effectively. We utilize ResNet50 and ViT architectures with Faster-RCNN for detection and recognition, developed using PyTorch 2.0. The dataset comprises 2000 samples, and 30% and 60% labeled data are employed. Experimental results show significant improvements in model performance and training efficiency, demonstrating the method’s effectiveness in complex power grid environments. Our approach enhances safety monitoring and advances active learning and hard sample techniques in practical applications.