Anomaly Detection of Distribution Network Synchronous Measurement Data Based on Large Dimensional Random Matrix

In order to identify faults of power system measurement subsystem, a method based on deep belief network is proposed in this paper. Firstly, data from actual measurement system is collected and divided into training and test samples. And then, the data is used to train a deep belief network. Finally, the model’s fault diagnosis results and actual samples’ labels are combined as a cross-validation set to test the deep belief network. The results show that the method based on deep belief network proposed in this paper can be more stable and reliable identification of electric power measurement equipment fault diagnosis.