Transformer substation secondary circuit fault positioning method and system based on graph neural network

A secondary circuit fault, neural network technology, applied in neural learning methods, biological neural network models, fault locations, etc., can solve problems such as retraining models, difficult applications, and difficult to deal with large networks, to avoid modeling, The effect of increased accuracy, improved accuracy and robustness

Active Publication Date: 2021-10-08
STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

In the secondary system of smart substation, if a node in the network topology fails, it will often cause abnormalities in other nodes connected to it, and then generate a large number of alarms, making it difficult to judge the source of the fault
[0003] There have been studies on the fault location methods of the secondary system of smart substations. Traditional methods mainly

Method used

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  • Transformer substation secondary circuit fault positioning method and system based on graph neural network
  • Transformer substation secondary circuit fault positioning method and system based on graph neural network
  • Transformer substation secondary circuit fault positioning method and system based on graph neural network

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Embodiment 1

[0055] Please refer to Figure 1 to Figure 4 , the embodiment of the present invention provides a substation secondary circuit fault location method based on graph neural network, including:

[0056] S1: Analyze the substation configuration description (SCD) of the smart substation, store the analysis results in the graph database, and establish the corresponding relationship between the physical circuit and the virtual circuit of the secondary equipment;

[0057] S2: use the historical database to make a training set in the form of the graph database, and train the graph neural network model offline, or use the fault emergence method to make a training set, and train the graph neural network model offline;

[0058] S3: Extract and analyze different alarm signals and network topology information generated by the secondary system;

[0059] S4: Use the alarm signal to find all associated faulty equipment, and preprocess the alarm signal to determine whether the associated fault...

Embodiment 2

[0090] Please refer to Figure 1 to Figure 5 , an embodiment of the present invention provides a substation secondary circuit fault location system based on a graph neural network, including:

[0091] The graph database production module is used to analyze the configuration description file of the smart substation, store the analysis results in the graph database, and establish the corresponding relationship between the physical circuit and the virtual circuit of the secondary equipment;

[0092] The model training module is used to use the historical database to make a training set in the form of the graph database, to train the graph neural network model offline, or to make a training set using the fault emergence method, to train the graph neural network model offline;

[0093] The analysis module is used to extract and analyze different alarm signals and network topology information generated by the secondary system;

[0094] A preprocessing module, configured to use the ...

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Abstract

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.

Description

technical field [0001] The invention relates to the technical field of intelligent substation fault location, in particular to a method and system for fault location of a secondary circuit of a substation based on a graph neural network. Background technique [0002] With the rapid development of smart substation information technology, the secondary system network of smart substations is becoming more and more complex. As the core of intelligent operation and maintenance of smart substations, fault location technology has always been a research hotspot. Due to the interconnection of secondary equipment, there is a certain correlation between the faults of the secondary circuit, and the fault location of the secondary circuit needs to locate the fault source from multiple fault events based on the relationship between the alarm signals. In the secondary system of smart substation, if a node in the network topology fails, it will often cause abnormalities in other nodes conne...

Claims

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Application Information

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IPC IPC(8): G01R31/08
CPCG01R31/088G06N3/04G06N3/08Y04S10/52
Inventor 郑永康张宸滔董秀成陈晓东李梓玮王海东刘勇赵以兵张豪杨伟沈大千赵梓宏杨凯张家兴罗俊陈桂芳朱鑫向贤明郭泓达
Owner STATE GRID SICHUAN ELECTRIC POWER CORP ELECTRIC POWER RES INST
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