A Distributed Secure State Estimation Framework for Unbalanced Active Distribution Systems

作者：Junjun Xu, Sheng Zhang, Tong Lin, Yusen Jiang, Xinyu Ruan, Shuheng Wei, Qinran Hu, Xinghuo Yu · 发表于：IEEE Transactions on Smart Grid · 年份：2025 · DOI：10.1109/tsg.2025.3587483 · 被引用次数：10 · 研究领域：Smart Grid Energy Management、Smart Grid Security and Resilience、Power Line Communications and Noise

The integration of cyber-physical systems (CPS) in active distribution systems (ADS) has heightened the risk of data explosion and malicious attacks, particularly false data injection attacks (FDIA), which will affect accurate awareness of the system operation status. This paper proposes a novel distributed secure state estimation (SE) framework, it segments the large-scale unbalanced ADS into manageable subareas using a community detection algorithm, and establishes a general imperfect FDIA model to simulate real-world attack scenarios, considering the different encryption levels of measuring devices. An improved unscented Kalman filter (IUKF) based local forecasting-aided SE algorithm is introduced to detect and mitigate the impact of FDIA by increasing measurement redundancy and collaboratively processing state predictions with measurements. A distributed computing strategy is employed to solve global secure SE models, ensuring robust interactions between adjacent subareas through boundary state exchanges. Case studies using the modified IEEE 123-bus test system and the real-world 300-node test system demonstrate that the proposed framework outperforms existing centralized and static SE models in combating FDIA, offering improved accuracy and resilience in system estimated results.