Real-Time Small-Signal Security Assessment Using Graph Neural Networks

TL;DR

This paper introduces a graph neural network approach for real-time small-signal security assessment in power systems, leveraging grid structure and limited PMU data to improve efficiency and reduce training times.

Contribution

The paper presents a novel GNN-based method for security assessment that exploits power grid topology and enables effective operation with partial PMU data.

Findings

GNN approach reduces training time compared to traditional methods.

Method performs well with limited PMU data.

Effective in simulated IEEE and NPCC systems.

Abstract

Security assessment is one of the most crucial functions of a power system operator. However, growing complexity and unpredictability make this an increasingly complex and computationally difficult task. In recent times, machine learning methods have gained attention for their ability to handle complex modeling applications. Some methods proposed include deep learning using convolutional neural networks, decision trees, etc. While these methods generate promising results, most methods still require long training times and computational resources. This paper proposes a graph neural network (GNN) approach to the small-signal security assessment problem using data from Phasor Measurement Units (PMUs). Leveraging the inherently graphical structure of the power grid using GNNs, training times can be reduced and efficiency improved for real-time application. Also, using graph properties, optimal PMU placement is determined and the proposed method is shown to perform efficiently under partial observability with limited PMU data. Case studies with simulated data from the IEEE 68-bus system and the NPCC 140-bus system are used to verify the effectiveness of the proposed method.