Convolutional Neural Network for Voltage Sag Source Azimuth Recognition in Electrical Internet of Things

Abstract

Recognition and analytics at the edge enable utility companies to predict and prevent problems in real time. Clearing the voltage sag disturbance source by the positioning method is the most effective way to solve and improve the voltage sag. However, for different grid structures and fault types, the existing methods usually achieve a sag source location based on the single feature of monitoring data extraction. However, due to the effectiveness and applicability of the existing method features, this paper proposes a multidimensional feature of the voltage sag source positioning method of the matrix. Based on the analysis of the characteristics of the voltage sag event caused by the fault, this paper proposes a multidimensional feature matrix for voltage sag source location, based on the convolutional neural network to establish the mapping relationship between the feature matrix and the voltage sag position, thus achieving multiple points based on multiple points. The voltage sag source orientation is identified by the monitoring data. Finally, the voltage sag event caused by the short-circuit fault is simulated in the IEEE14 node model, and the effectiveness of the proposed method is verified by simulation data. The simulation results show that the proposed method has higher accuracy than the traditional method, and the method can be applied to different grid structures and different types of faults.