Localization of False Data Injection Attacks in Smart Grids With Renewable Energy Integration via Spatiotemporal Network

• Published in: IEEE internet of things journal p. 1
• Main Authors: Yu, Yang, Liu, Chensheng, Xiong, Luolin, Tang, Yang, Qian, Feng
• Format: Journal Article
• Language: English
• Published: IEEE 17.08.2024
• Subjects: attack localization; Correlation; False data injection attacks; Feature extraction; Location awareness; machine learning; Power systems; renewable energy; Smart grids; spatiotemporal correlation; State estimation; Task analysis
• ISSN: 2327-4662
• DOI: 10.1109/JIOT.2024.3436520

The precise localization of False Data Injection Attacks (FDIA) is vital to ensure the stable operation of smart grids. However, the intermittency and uncertainty of renewable energy can lead to confusion with unknown FDIA. As a result, previous works encountered difficulties in extracting distinguishable spatiotemporal features to construct accurate behavior models, thereby affecting the effectiveness of the localization task. To address this challenge, we establish a more practical dataset for FDIA localization that takes renewable energy into account. Subsequently, we propose a spatiotemporal sequence analysis framework for the task. Specifically, we propose a factorized module to mitigate the impact of temporal fluctuations, which processes data sequence with down sampling and feature aggregation. Additionally, we introduce a fine-tuning matrix to take regional correlations of renewable energy into consideration, where the weights of spatial information aggregation are adjusted. We evaluate the effectiveness of our approach through comprehensive case studies on IEEE 14-bus, IEEE 57-bus, and IEEE 118-bus standard test systems. The experimental results indicate that our method outperforms the compared methods by an average of 2.52% and 3% in terms of recall and F1-score, respectively.