# A GNN-Based False Data Detection Scheme for Smart Grids
## 1 Introduction
This study [8] proposed to analyze the mapping of non-linear relationships from the categories of dissolved gases to the categories of transformer faults using Graph Convolutional Networks (GCNs). However, after severe weather or dissolved gases were generated and the threat to the grid emerged, these methods could not address the possible threats and simply construct a mapping among weather, dissolved gases, and anomalous events of the smart grid. MAD-SGCN [9] efficiently captures both temporal and spatial dependencies in the input sequence via Long Short-Term Memory networks (LSTMs) and GCNs, and determines whether anomalies are currently occurring by reconstructing the degree of the observed sequence. However, the method uses LSTM to obtain the temporal correlation of the input sequence and does not use the possible state of the future sequence to determine the potential threat. Figure 1: Several information networks with the same topology measured by power network sensors. In other CPSs, such as the physical transmission network, the topology of the network tends to change to different degrees with the passage of time or the occurrence of anomalies; however, in smart grids, changes in the power network topology often indicate that anomalies have occurred, and dynamic changes in the power network topology may lack practical application for anomaly detection. Therefore, the traditional spatial-temporal GNN-based anomaly detection model for CPSs cannot be applied directly to the smart grid. As shown in Figure 1, the temperature, wind, rainfall, and other data measured by the sensors naturally conform to the graph signal processing process, and the weather and other external factors measured by the sensors are naturally interdependent in time and space and have an impact on the power load of the smart grid. Therefore, our research could utilize the spatial temporal graph neural network (STGNN) to manage the information network composed of these external factors as well as the evolution of the information network to represent the change of the smart grid state and forecast the potential external threats and anomalies in the future. As shown in Figure 2, smart grids are divided into communication networks, power networks, and physical transmission networks. In the power network, distributed power units are one of the core components of the entire system. Each unit module integrates power electronic converters, sensing devices, and control units. Each unit module includes a power converter, inverter, DC bus, and intelligent switch. The power generation or load unit is connected to the busbar by power electronic converters and intelligent switches. Each unit is interconnected through a network, with sensors in one unit connected to controllers in another unit. Sensors collect physical data from various units and send it to the communication network. The controller then issues corresponding control commands from the communication network. In the physical transmission network, energy flows through the power grid, thereby achieving the generation, transmission, distribution, and application of electrical energy. Photovoltaic power generation, wind turbines, and other means of power generation, as well as energy storage and loads, constitute the key physical network of the entire energy system. The integrated energy system consists of multiple physical subsystems, including photovoltaic power stations, wind turbines, diesel generators, energy storage devices, electric vehicle charging facilities, and distributed energy supply equipment, among others. The common coupling point is the connection method between the integrated energy system and the distribution network. In the communication network, information flow is accomplished through communication networks for communication, computation, analysis, and decision-making. The communication network is the link of the digital world, carrying the exchange of energy and wisdom.