Abstract
Abstract In distribution automation systems, detecting terminal abnormal behaviors is crucial for stability and reliability. Traditional methods struggle with insufficient feature extraction and weak generalization when handling multi-modal data. Thus, an anomaly detection method based on self-attention convolutional neural network (SA-CNN) is proposed, integrating the strengths of self-attention mechanisms and convolutional networks to enhance detection capabilities. Experiments on the IEEE PHM dataset demonstrate superiority over traditional CNN and ARIMA algorithms, achieving accuracy, recall, and F1 scores of 0.928, 0.936, and 0.932, respectively. Future work aims to improve model efficiency and performance.
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