Data-Driven-Based Detection and Localization Framework Against False Data Injection Attacks in DC Microgrids

In response to carbon peaking and carbon neutrality, DC microgrids (MGs), as a key pillar, have facilitated efficient and reliable power transmission between renewable energy sources, energy storage devices, and various loads. In the process, the heavy reliance on communication networks exposes them to potential cyber-physical security risks. Namely, attackers can inject false data to achieve current or voltage overload without triggering an alarm by eavesdropping the communication data between the converter and MG center. For this reason, an attack detection and localization framework using data-driven is constructed in this article. Utilizing the subspace identification methods, a data-driven I/O model aiming at sketch the process input-output data-based framework for DC-MG dynamic processes is established. Afterward, the necessary theory on the data collected for the observability and controllability of the proposed data-driven model is given. Based on this, an attack detection and localization framework for data-driven design of DC-MG system is presented. The proposed framework includes a bank of adaptive residual generators, adaptive detection threshold and localization observers, whose parameters can directly be obtained from process data. Finally, simulation tests on the meshed DC-MG system consisting of four distributed generation units are presented to demonstrate the superiority of the developed attack detection and localization framework.