Detection and Localization of False Data Injection Attacks in Smart Grids: A Spatiotemporal Feature-Fusion Deep-Learning Method Based on Gray Wolf Optimizer

False Data Injection Attacks (FDIA) pose a serious threat to smart grid security due to their high concealment. In view of the defect that existing detection methods for FDIA in smart grids can only judge whether an attack occurs but fail to accurately locate attacked grid nodes, this study proposes a specialized attack localization and detection framework. To address the node-level class imbalance inherent in attack localization, we design a node-adaptive weighting strategy tailored for multi-label classification. Furthermore, we employ a Hadamard-product-based deep fusion mechanism to integrate spatial and temporal features, which, unlike simple concatenation, enables a more profound feature interaction. The framework is optimized using the gray wolf optimizer (GWO) to enhance convergence and stability. In this method, a graph convolutional network (GCN) is used to extract spatial topological correlation features of power grid measurement data, and a bidirectional long short-term memory (BiLSTM) is adopted to mine temporal dependency features of time-series data. The deep fusion of spatial and temporal features is realized through the Hadamard product. Meanwhile, the GWO is introduced for global optimization of model hyperparameters to optimize network performance and further improve detection and localization accuracy. Simulation results on the IEEE 14-bus and IEEE 118-bus power systems show that the GWO-STFFN model surpasses existing comparative models in key indicators, including detection accuracy, F1-Score, and AUC value, delivering higher node localization precision and lower Hamming Loss. In addition, it maintains favorable robustness under different noise intensities.