Power electronics-intensive dc microgrids use increasingly complex software-based controllers and communication networks. They are evolving into cyber-physical systems (CPS) with sophisticated interactions between physical and computational processes, making them vulnerable to cyber attacks. This paper presents a framework to detect possible false-data injection attacks (FDIAs) in cyber-physical dc microgrids. The detection problem is formalized as identifying a change in sets of inferred candidate invariants. Invariants are microgrids properties that do not change over time. Both the physical plant and the software controller of CPS can be described as Simulink/Stateflow (SLSF) diagrams. The dynamic analysis infers the candidate invariants over the input/output variables of SLSF components. The reachability analysis generates the sets of reachable states (reach sets) for the CPS modeled as hybrid automata. The candidate invariants that contain the reach sets are called the actual invariants. The candidate invariants are then compared with the actual invariants, and any mismatch indicates the presence of FDIA. To evaluate the proposed methodology, the hybrid automaton of a dc microgrid, with a distributed cooperative control scheme, is presented. The reachability analysis is performed to obtain the reach sets and, hence, the actual invariants. Moreover, a prototype tool, HYbrid iNvariant GEneratoR, is extended to instrument SLSF models, obtain candidate invariants, and identify FDIA.

Emerging converter-dominated dc microgrids employ distributed cooperative control strategies and communication network. Since there is no central entity to monitor and assess the global cyber scenario, microgrids employing distributed control are prone to cyber attacks. This work presents signal temporal logic (STL) detection of two major types of cyber attacks, namely false-data injection attacks and denial-of-service attacks. Such cyber attacks can compromise voltage regulation and load sharing in dc microgrids. STL is a formalism to monitor the output voltages and currents of dc microgrids against the defined specifications, such as operational bounds, over time. Besides detection, the proposed approach also quantifies the attack impact. Moreover, it can be effectively employed for a complex dc microgrid without prior knowledge of its dynamics. This detection technique is successfully demonstrated using a physical microgrid setup or in a hardware-in-the-loop environment, where various attacks are formalized, detected, and quantified.

This paper considers a cooperative and adversarial AC microgrid system consisting of cooperative leaders and inverters, as well as adversarial attackers. The attackers aim to destabilize the synchronization dynamics of the AC microgrid by first intercepting the communication channels, penetrating the local state feedback, and pretending to be a cooperative neighbor, and then initiating malicious attacks by launching unbounded injections. A fully distributed resilient control framework is offered for the secondary frequency regulation and voltage containment to ensure system stability and preserve bounded synchronization. In particular, a virtual resilient layer with hidden networks is developed to integrate with the original cyber-physical layer. The proposed resilient control framework is fully distributed without requiring any global information. A modified IEEE 34-bus test feeder benchmark system is emulated in a controller/hardware-in-the-loop environment, where the control objectives are met under different attack scenarios.

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

This paper presents hybrid automaton modeling, comparative model validation, and formal verification of stability through reachability analysis of pulse width modulation (PWM) dc-dc converters. Conformance degree provides a measure of closeness between the proposed hybrid automata models and experimental data. Nondeterminism due to variations in circuit parameters is modeled using interval matrices. In direct contrast to the unsound and computationally-intensive Monte Carlo simulation, reachability analysis is introduced to overapproximate the set of reachable states and ensure stable operation of PWM dc-dc converters. Using a 200 W experimental prototype of a buck converter, hybrid automata models of open-loop, and hysteresis-controlled converters are first validated against experimental data using their conformance degrees. Next, converter stability is formally verified through reachability analysis and informally validated using Monte Carlo simulations and experimental results.

Modern cyber-physical microgrids rely on the information exchanged among power electronics devices (i.e., converters or inverters with local embedded controllers) making them vulnerable to cyber manipulations. The physical devices themselves are susceptible to potential faults and failures. Effects of these cyber and physical anomalies can propagate throughout the entire microgrid due to information exchanged and the inherent low inertia of the distribution network. This work employs the parametric time-frequency logic (PTFL) framework to detect such cyber-physical anomalies. PTFL is a formalism to analyze the time-frequency content of the observable quantities of interest (such as current, voltage, or frequency) of power electronics devices in comparison with the predefined time-frequency properties. PTFL formalism is presented to detect the anomalies such as false data-injection attacks, denial-of-service attacks, and faults on a cluster of four DC microgrids and an inverter-populated IEEE 34-bus feeder system in a controller/hardware-in-the-loop environment.