变电站多尺度异常入侵目标轻量化检测方法OA

The construction concept of smart grids has determined the trend of unmanned monitoring of anomaly invasion targets in suburban substations,promoting the rapid development of intelligent detection methods for anomaly invasion targets in substations.However,at present,there is no dataset specifically designed for anomaly invasion targets in this scenario,and the existing target detection methods have not been optimized for lightweight design on the edge computing end of substations,which is not suitable for real-time monitoring of substation edge devices that require round-the-clock monitoring.To address these issues,starting from practical application requirements,a Dataset for Anomaly Invasion Targets in Substations(SAITD)is constructed,and a lightweight anomaly invasion target detection network,YOLOv5-Substation,which is suitable for substation edge detection devices is proposed based on the YOLOv5s model.A micro-scale target feature extraction layer and an upsampling lightweight operator CARAFE are added to expand the receptive field while fully preserving the semantic information of multi-scale targets in the feature map,improving the detection accuracy of the original model from the architecture end.Based on the knowledge distillation model,the original model is lightweighted using Network-slimming strategies to ensure the detection accuracy of the original model while accelerating model inference.Simulation experiments show that the accuracy of the lightweighted edge-end computing model is 3.3％higher than that of YOLOv5s,and the inference speed is 41.9％faster,providing a strong data foundation,technical support and security guarantee for the full-speed operation of smart grids.