Detection of Multi‐Target False Data Injection Attacks in Networked Microgrids Considering Renewable Energy Uncertainties

To address the detection issue of false data injection attacks (FDIAs) in networked microgrids (NMGs) with renewable energy integration and secondary frequency regulation, this paper proposes a novel detection scheme based on delay unknown input observers (DUIOs). Firstly, a comprehensive dynamic NMG model is developed under the cyber‐physical integration framework, incorporating renewable energy systems, hybrid energy storage systems and dynamic frequency control for multi‐microgrid interactions. This model establishes a foundation for analysing multi‐target FDIAs across different channels and control areas. Secondly, an innovative DUIO is designed that incorporates renewable energy uncertainties and load prediction errors into the observer design, achieving accurate system state estimation under unknown disturbances through the introduction of an observer delay parameter. Finally, a multi‐target detection framework based on residual analysis is proposed, enabling precise attack localisation across different channels and regions through multiple parallel observers and a moving accumulation residual evaluation mechanism. Simulations conducted on a three‐area NMG validate the effectiveness of the proposed scheme under various attack scenarios, including single‐area multi‐target attacks, multi‐area multi‐target attacks and coordinated attacks, while maintaining robustness against renewable energy fluctuations.