Spatio-temporal graph neural network for state awareness and fault diagnosis in virtual power plant communication networks

Abstract

Power communication networks in virtual power plants (VPPs) play a critical role in enabling reliable information exchange among distributed energy resources, energy storage systems, controllable loads, and dispatch centers. However, their complex topology, strong inter-node dependency, and hidden fault propagation paths make accurate state awareness and fault diagnosis highly challenging for conventional rule-based and shallow learning methods. To address this issue, this paper proposes a graph neural network (GNN)-based state awareness and fault diagnosis method for power communication networks in VPPs. First, a node-link coupled graph representation is constructed by integrating network topology and multi-source operational data, so as to capture the correlations among devices, communication links, and service states. Then, a GNN-based framework is developed to learn discriminative state features through neighborhood aggregation and graph representation learning, enabling the identification of multiple fault types, including link anomalies, node failures, and local congestion. In addition, temporal state evolution is incorporated to improve the model’s ability to characterize dynamic operating conditions and complex disturbances. Experimental results demonstrate that the proposed method outperforms conventional machine learning and mainstream deep learning approaches in terms of state recognition accuracy, fault diagnosis precision, and robustness. The proposed method provides an effective solution for intelligent monitoring, online state awareness, and fault early warning in VPP communication networks.