FDIA localization and classification detection in smart grids using multi-modal data and deep learning technique

Abstract

False data injection attacks (FDIA) exploit the vulnerabilities of bad data detection in energy management systems to maliciously tamper with state estimation results, seriously jeopardizing the safe and reliable operation of power systems. In order to promptly detect FDIA, recent studies have used machine learning techniques to extract attack characteristics and detect FDIA based on changes in these characteristics. Current research predominantly focuses on detecting a single type of FDIA attack model and topology. However, the increasing diversification of FDIA construction methods and the variability of topological structures in power systems can reduce or even invalidate detection effectiveness. To cope with the abovementioned difficulties, this study introduces a multimodal deep learning detection model based on variational graph auto-encoders (VGAE), temporal convolutional networks (TCN), and gated recurrent units (GRU). The topological features of power systems are obtained through VGAE to adapt to the detection needs of various topological structures and performance enhancement. These features are then integrated with preprocessed measurement data via BDD to form multimodal data. Furthermore, this multimodal data is used to locate and classify FDIA with complete information, FDIA with incomplete information, and topology attack after applying a multi-label classification algorithm based on TCN-GRU for temporal feature extraction. Experiments carried out on the IEEE 14 and IEEE 118 bus systems show that the proposed method is robust and has high detection performance. The results indicate that the proposed detection method achieves AUC values that are, on average, 0.085, 0.145, and 0.04 higher than those of CNN, LSTM, and CNN-LSTM, respectively. Moreover, under various noise and attack intensities, recall, precision, F1 score, and RACC remain above 0.859, 0.877, 0.867, and 0.818, respectively, with a classification accuracy greater than 0.912. This study provides a unique perspective on detecting FDIA across various attack models and power system topologies.