Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks

Consensus of First Order Multi-agent Systems with Actuator or dynamic Fault by weighted adjacency matrix approach (WAMA)

In this paper, the consensus problem for the first-order discrete-time multi-agent systems (MASs) In the presence of fault is studied. This study uses two concepts of watchdog model-based observer and weighted adjacency matrix approach (WAMA). The purpose of this study is to reach a consensus on MASs when one or more agents have been damaged. In this method, for any agent, a watchdog that measures the amount of difference between the states of the actual system and the calculated states of the system by the agent\u27s model is designed, this generated residual declares the amount of agents deviation. A decision function is used to decide how this malfunctioning affects the neighboring agents. Therefore, the states that each agent transmits to the neighboring agents are augmented by the output of the decision function. By increasing states matrix dimensions, each agent will report its health status or commanding by using a new state to the neighboring agents. This additional state is affected by a decision function that expresses the neighboring agents how should behave with this malfunction agent. Finally, the produced residue will be weight of the adjacency matrix and will cause to correct consensus. Simulation results are presented to illustrate the effectiveness of this approach