Power System Network Topology Identification Based on Knowledge Graph and Graph Neural Network

Changgang Wang,Jun An,Gang Mu

doi:10.3389/fenrg.2020.613331

Changgang Wang, Jun An + Show 1 more

Open Access

https://doi.org/10.3389/fenrg.2020.613331

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Abstract

The automatic identification of the topology of power networks is important for the data-driven and situation-aware operation of power grids. Traditional methods of topology identification lack a data-tolerant mechanism, and the accuracy of their performance in terms of identification is thus affected by the quality of data. Topology identification is related to the link prediction problem. The graph neural network can be used to predict the state of unlabeled nodes (lines) through training on features of labeled nodes (lines) with fault tolerance. Inspired by the characteristics of the graph neural network, we applied it to topology identification in this study. We propose a method to identify the topology of a power network based on a knowledge graph and the graph neural network. Traditional knowledge graphs can quickly mine possible connections between entities and generate graph structure data, but in the case of errors or informational conflicts in the data, they cannot accurately determine whether the relationships between the entities really exist. The graph neural network can use data mining to determine whether a connection obtained between entities is based on their eigenvalues, and has a fault tolerance mechanism to adapt to errors and informational conflicts in the graph data, but needs the graph data as database. The combination of the knowledge graph and the graph neural network can compensate for the deficiency of the single knowledge graph method. We tested the proposed method by using the IEEE 118-bus system and a provincial network system. The results showed that our approach is feasible and highly fault tolerant. It can accurately identify network topology even in the presence of conflicting and missing measurement-related information.

Highlights

With access to new energy sources continuously increasing and the scale of power grids growing, the variability and complexity of the operation of power grids has increased drastically (Li et al, 2018)
We combined the knowledge graphs and the graph neural networks to identify the topology of the power network
Considering that the training time increased with the amount of input data, we trained the samples with a window length of 10 min and a step length of 10 min, used the number of line features in period t as input, and determined the network topology in period t+1 based on the results of training

Summary

Introduction

With access to new energy sources continuously increasing and the scale of power grids growing, the variability and complexity of the operation of power grids has increased drastically (Li et al, 2018). With the rapid development of power grid measurement systems and the increasing maturity of big data technology, the state cognition and control of grid operations based on operational information on power grids has emerged as the new model for their secure operation (Liu et al, 2018). Several studies have used the incidence matrix and the adjacency matrix constructed by using the switching state of the system to determine its connectivity and track topological changes (Zhu et al, 2011; Ma et al, 2014; Lourenco et al, 2015) Such methods are less tolerant of faults and conflicting telemetry data, and their effect depends entirely on the quality of remote signaling data. It is a new attempt to identify network topology based entirely on data The feasibility of this approach has been verified. We need a fault-tolerant method of topology identification

Methods

Results

Conclusion