Distributed state estimation-based resilient controller design for IoT-enabled microgrids under deception attacks

L. Ponnarasi, P.B. Pankajavalli, Yongdo Lim and R. Sakthivel

Applied Energy, 2024, vol. 374, issue C, No S0306261924013801

Abstract: This paper addresses the distributed state estimation-based centralized control design problem for Internet-of-Things (IoT)-enabled microgrid systems in the presence of deception attacks. Firstly, a microgrid with multiple synchronous generators is described in a state-space model. Furthermore, the states of the microgrid power system are monitored by using a set of sensors linked through IoT-enabled networks, where attacks are presumed to occur during measurements. In particular, the deception attacks are capable of compromising grid security through the malicious manipulation of measurement data. To tolerate this effect in this paper a resilient distributed state estimation design based control algorithm is developed. The microgrid system is then controlled by a centralized scheme that uses only estimates generated by a certain group of IoT-enabled networked nodes. Subsequently, an augmented system is developed that incorporates both the microgrid system and the estimator error dynamics. Using Lyapunov stability theory and matrix inequality approach, the proposed local and neighboring estimator based control gains are designed to ensure the accurate estimation of load and energy source output and continuous power supply. Specifically, the particle swarm optimization algorithm is used for optimizing the distributed state estimation-based controller to achieve the desired performance. Finally, simulation results demonstrate that the developed algorithm is capable of estimating the state of the microgrid and controlling its operations. The result reveals that microgrids can provide a constant flow of electricity while being resistant to deception attacks and disruptions caused by the networks.

Keywords: Internet-of-Things; Microgrid; Resilient state estimation; Deception attacks; Particle swarm optimization algorithm (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123997

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