Deep Learning Based Detection of Line Loss Anomalies in Power Grids

With the continuous promotion of smart grid construction, the problem of grid line loss has become an important factor affecting power supply quality and operational efficiency. Traditional line loss analysis methods have problems such as low detection accuracy, response lag, and difficulty in adapting to large-scale complex power grids. For this reason, this paper carries out a systematic research on abnormal detection of power grid line loss based on deep learning technology. Firstly, this paper systematically analyses the basic principle of grid line loss and its abnormal types; secondly, at the data level, this paper designs the data preprocessing process of line loss anomaly detection based on SCADA, AMI and other system data; finally, this paper proposes a line loss prediction model based on LSTM and introduces an attention mechanism to improve its ability to capture key temporal features, and further integrates the multimodal input to to construct a comprehensive detection system.