DST-GNN: A Dynamic Spatiotemporal Graph Neural Network for Cyberattack Detection in Grid-Tied Photovoltaic Systems

The increasing deployment of solar photovoltaic (PV) systems in the electric grid, aimed at addressing the energy crisis and surging power demands, has expanded the potential vulnerability to cyberattacks due to the inter-networking of the grid-connected power electronics converters. In this paper, we propose a dynamic spatiotemporal graph neural network (DST-GNN) for cyberattack detection in grid-tied PV systems. Specifically, to exploit the inherent graph topology of the grid-tied PV system, we start by employing a GNN with a dynamic weighted adjacency matrix to capture the latent spatial correlations within signal data. Then, a one-dimensional convolution neural network (1D-CNN) is utilized to extract the underlying temporal patterns. Notably, we leverage the system dynamics to determine the dynamic graph weights and the number of graph convolution layers, while the hyper-parameters of 1D-CNN are designed based on the periodicity of input signals. Finally, the integration of the priori physical system knowledge further enhances the interpretability and improves the detection performance of DST-GNN. To the best of our knowledge, this is the first work that embeds the grid-tied PV system into a graph structure for cyberattack detection. The effectiveness of DST-GNN is evaluated through comprehensive case studies on a hardware-in-the-loop (HIL) grid-tied PV testbed, and numerical results demonstrate its superiority over baseline methods.