Identification and correction of abnormal measurement data in power system based on graph convolutional network and gated recurrent unit

Abstract

Power system measurement data is used as the basis for various power system analyses. Its accuracy and reliability are particularly important. However, due to many problems, there are abnormal data in the measurement data. An identification method based on the spatial-temporal characteristics of measurement data is proposed to resolve the problems of poor identification and low efficiency. The Pre-identification (PI) process used to improve efficiency is introduced, and the sliding window based on the slope is used to realize rapid interval targeting of abnormal data and generate suspicious data sets. A spatiotemporal fusion model based on Graph Convolutional Network (GCN) and Gated Recurrent Unit (GRU) has been proposed. By aggregating the spatial-temporal characteristics of measurement data, precise reconstruction of measurement data is achieved. Then, by setting a threshold to separate the real abnormal data and normal data, the suspicious data set can be cleaned. By simulation experiments, under the cases of different ratios and different types of abnormal data, the proposed method is proved to be better in identification performances and higher in efficiency. By testing actual measurement data, the proposed method can accurately identify abnormal data under the interference of fluctuating data, indicating the proposed method has good robustness.