The invention discloses a
transformer substation
secondary circuit fault positioning method and
system based on a graph neural network, wherein the method comprises the steps: analyzing a configuration description file of an intelligent
transformer substation, storing an analysis result into a
graph database, and building a corresponding relation between a physical circuit and a
virtual circuit of secondary equipment; making a
training set by using a historical
database or a fault emergence method, and training a graph neural
network model offline; finding out all associated fault equipment by using an
alarm signal, preprocessing the
alarm signal, judging whether the associated fault equipment forms a connected graph or not, and if the connected graph is not formed, dividing the the associated fault equipment into independent connected graphs, representing
topological information and alarm signals of the independent connected graphs, and inputting the
topological information and alarm signals into a trained graph neural
network model; and predicting the fault type of the associated fault equipment by using the graph neural
network model. According to the method, the graph neural network is used for building the fault positioning model, so that the accuracy of the model is improved under the condition that the networking mode is changed.