MoE-GraphSAGE-Based Integrated Evaluation of Transient Rotor Angle and Voltage Stability in Power Systems

TL;DR

This paper introduces MoE-GraphSAGE, a graph neural network framework that improves the accuracy and efficiency of transient stability assessment in power systems with renewable energy integration.

Contribution

It presents a novel MoE-GraphSAGE model that jointly assesses transient rotor angle and voltage stability using spatiotemporal features and multi-expert networks.

Findings

Achieves higher accuracy than traditional methods

Demonstrates improved computational efficiency

Effective for online stability assessment

Abstract

The large-scale integration of renewable energy and power electronic devices has increased the complexity of power system stability, making transient stability assessment more challenging. Conventional methods are limited in both accuracy and computational efficiency. To address these challenges, this paper proposes MoE-GraphSAGE, a graph neural network framework based on the MoE for unified TAS and TVS assessment. The framework leverages GraphSAGE to capture the power grid's spatiotemporal topological features and employs multi-expert networks with a gating mechanism to model distinct instability modes jointly. Experimental results on the IEEE 39-bus system demonstrate that MoE-GraphSAGE achieves superior accuracy and efficiency, offering an effective solution for online multi-task transient stability assessment in complex power systems.