Resilient Temporal GCN for Smart Grid State Estimation Under Topology Inaccuracies

TL;DR

This paper proposes modifications to Temporal Graph Convolutional Networks to improve power system state estimation accuracy under topology uncertainties by integrating a data-driven knowledge graph.

Contribution

It introduces two TGCN architectures that incorporate a knowledge graph to enhance resilience against topology inaccuracies in power system state estimation.

Findings

Both architectures improve estimation accuracy under topology uncertainties.

The models demonstrate different performance trade-offs.

Knowledge graph integration enhances robustness of TGCN.

Abstract

State Estimation is a crucial task in power systems. Graph Neural Networks have demonstrated significant potential in state estimation for power systems by effectively analyzing measurement data and capturing the complex interactions and interrelations among the measurements through the system's graph structure. However, the information about the system's graph structure may be inaccurate due to noise, attack or lack of accurate information about the topology of the system. This paper studies these scenarios under topology uncertainties and evaluates the impact of the topology uncertainties on the performance of a Temporal Graph Convolutional Network (TGCN) for state estimation in power systems. In order to make the model resilient to topology uncertainties, modifications in the TGCN model are proposed to incorporate a knowledge graph, generated based on the measurement data. This knowledge graph supports the assumed uncertain system graph. Two variations of the TGCN architecture are introduced to integrate the knowledge graph, and their performances are evaluated and compared to demonstrate improved resilience against topology uncertainties. The evaluation results indicate that while the two proposed architecture show different performance, they both improve the performance of the TGCN state estimation under topology uncertainties.