用混合专家图网络统一评估电网暂态功角与电压稳定性
MoE-GraphSAGE-Based Integrated Evaluation of Transient Rotor Angle and Voltage Stability in Power Systems
- 基于MoE的图神经网络捕捉电网时空拓扑特征
- 在IEEE 39节点系统上实现高精度高效评估
- 适合需要在线多任务稳定评估的电力系统研究者
可再生能源和电力电子设备的大规模接入增加了电力系统稳定性的复杂性，使得暂态稳定评估更加困难。传统方法在准确性和计算效率方面均存在局限。为应对这些挑战，本文提出基于混合专家（MoE）的GraphSAGE框架（MoE-GraphSAGE），用于统一评估暂态功角稳定（TAS）与暂态电压稳定（TVS）。该框架利用GraphSAGE捕获电网的时空拓扑特征，并采用带门控机制的多专家网络联合建模不同失稳模式。在IEEE 39节点系统上的实验结果表明，MoE-GraphSAGE在准确性和效率上均表现优异，为复杂电力系统中的在线多任务暂态稳定评估提供了有效解决方案。
原文摘要 · Abstract (English)
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.
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