Research on Data Recognition of Power Distribution Network in Transformer District Based on Recurrent Neural Network

As the scale of collected data continues to increase, traditional anomaly detection methods are difficult to apply to large-scale online detection systems. Therefore, this paper uses the recurrent neural network (RNN) in deep learning theory to realize the anomaly detection of time series data in the power system. The models built in this paper have relatively high feasibility and effectiveness compared with the k-nearest neighbor (KNN) algorithm. The precision and the recall rate of abnormal data detection both reaches 98.6%. Finally, the data cleaning framework is proposed to process the data.