A Deep Iearning Based Method for Detecting Abnormal Line Loss in Distribution Networks
The detection of abnormal line losses in distribution networks is crucial for ensuring grid safety,optimizing resource allocation,and improving service quality.Traditional line loss analysis methods have limitations in handling large-scale datasets and identifying complex abnormal patterns.To address this issue,this article proposes a deep learning based method for detecting abnormal line losses in distribution networks.This method uses LSTM to perform temporal analysis on line loss data to capture the spatiotemporal characteristics of the data,achieving real-time monitoring and anomaly detection of distribution line losses.The experimental results show that this method has high detection accuracy and real-time performance,and can effectively identify line loss anomalies in the distribution network,providing strong technical support for the optimized operation and management of the distribution network.
deep learningdistribution networkabnormal detection of line losslong short-term memory network
NETL
NSTL
万方数据
