Graph database-based algorithm for analyzing N-1 anticipated faults on grid transmission lines

In a large-scale interconnected power grid, when a fault occurs in the grid, the system current will be redistributed, and if left untreated, the overloaded line or transformer will be removed, which may cause a series of chain reactions or even system collapse. The grid transmission line fault analysis based on graph database proposed in this paper mainly relies on the powerful relational query and storage capability of graph database. Firstly, by constructing the graph of transmission lines, it is able to integrate the line equipment and their relationship information with each other, and store various information of transmission lines, such as the geographic information of the lines, the equipment models, and the operation status, etc., in the form of a graph database. Secondly, the graph database is utilized in combination with graph convolutional network to construct a graph convolutional network based on the graph spectrum to quickly locate fault points. Finally, by analyzing the operating status, maintenance history and other information of the line equipment, the possible fault time and fault type of the equipment can be predicted, so that corresponding maintenance measures can be taken in advance to avoid the occurrence of faults. After testing, the speed of recognizing faults is significantly improved.