Resilient control of multi-microgrids against false data injection attack

Resilient distributed optimization for cyber-physical systems under adversarial environments: An event-based method

This work presents a resilient distributed optimization algorithm based on the event-triggering mechanism for cyber-physical systems (CPSs) to optimize an average of convex cost functions corresponding to multiple agents under adversarial environments. Two attack scenarios, including the f-total (each agent is affected by at most f malicious agents in the whole network) and the f-local (each agent is affected by at most f malicious agents in its in-neighbor set) attacks are considered. Subsequently, the convergence conditions under these two attack scenarios are provided, respectively, both of which guarantee that the state values of benign agents converge to a bounded error range. The optimality conditions are also presented by theoretical analysis, which guarantee that the state values of benign agents converge to a safety interval constructed by local optimal values under certain graph conditions, despite the misbehavior of malicious agents. In addition, four numerical examples are presented to show the effectiveness and superiority of the event-triggering resilient distributed optimization (RDO-E) algorithm. Compared to existing resilient algorithms, the proposed method achieves resilient distributed optimization with higher accuracy and less demanding communication overheads. Finally, by applying the proposed method to the multi-microgrid system, a resilient economic dispatch problem (REDP) is successfully solved, which validates the practical viability of the RDO-E algorithm.

Consensus-based secondary control for DC microgrids with communication delays via a networked predictive control strategy

In today's cyber-physical microgrid systems, the consensus-based secondary control is generally utilized to settle the voltage deviation and rough current allocation issues at the primary control level. However, time delays follow inevitably the introduction of sparse communication networks, and most existing works adopt passive tolerance approaches. To actively alleviate the unavoidable delay effect in microgrids' communication networks, a networked predictive control (NPC) strategy is proposed for an islanded DC microgrid subject to time delays in this paper. Firstly, the predictive approaches for both voltage and current are developed based on the cyber-physical microgrid model. Unlike the practice of passively tolerating time delays, the NPC strategy is proposed to actively compensate for the effect of communication delays by estimating real-time voltage and current values using the previously obtained prediction models. Moreover, to prove the generality of the developed method, the microgrid systems' stability can be derived from the Schur stability of the closed-loop system, thus the DC microgrid can achieve voltage regulation and proportional current sharing simultaneously. Finally, the performance of our method against the time delay effect is validated by extensive experiments on an islanded 48-V DC microgrid system, in terms of its feasibility, delay tolerance ability, and robustness to load changes and communication faults. Experimental results demonstrate the effectiveness and superiority of the NPC strategy.

A hierarchical framework for distributed resilient control of islanded AC microgrids under false data injection attacks

In this paper, we propose a control scheme to ensure the microgrid control layers are resilient to cyberattacks. The studied microgrid consists of several distributed generation (DG) units and we consider the hierarchical control structure that is common for microgrids. The use of communication channels among DGs has made microgrids more vulnerable and this is where cybersecurity issues arise. In this work we added three algorithms, reputation-based, Weighted Mean Subsequence Reduced (W-MSR) and Resilient Consensus Algorithm with Trusted Nodes (RCA-T), to the secondary control layer of the microgrid and made them resilient to false data injection (FDI) attacks. In reputation-based control, some procedures are used for detecting the attacked DGs and isolating them from the others. W-MSR and RCA-T are Mean Subsequence Reduced (MSR)-based algorithms that fade the effect of attacks without finding them. These algorithms use a simple strategy that ignores some extreme values of neighboring agents, so an attacker can simply get ignored. Our analysis of the reputation-based algorithm is based on scrambling matrices, so the communication graph can switch in a prescribed set. In each of the above cases, to evaluate the performance of the designed controllers, in addition to theoretical analysis, we evaluated and compared the controllers using simulation.

An anti-consensus strategy based on continuous perturbation updates in opposite directions

In modern society, multi-agent consensus is applied in many applications such as distributed machine learning, wireless sensor networks and so on. However, some agents might behave abnormally subject to external attack or internal faults, and thus fault-tolerant consensus problem is studied recently, among which Q-consensus is one of the state-of-the-art and effective methods to identify all the faulty agents and achieve consensus for normal agents in general networks. To fight against Q-consensus algorithm, this paper proposes a novel strategy, called split attack, which is simple but capable of breaking consensus convergence. By aggregating all the states of neighboring nodes with an extra perturbation, the normal nodes are split into sub-groups and converge to two separate values, so that consensus is broken. Two scenarios, including the introduction of additional faulty nodes and compromise of the original nodes, are considered. More specifically, in the former case, two additional faulty nodes are adopted, each of which is responsible to mislead parts of the normal nodes. While in the latter one, two original normal nodes are compromised to mislead the whole system. Moreover, the compromised nodes selection is fundamentally a classification problem, and thus optimized through CNN. Finally, the numerical simulations are provided to verify the proposed schemes and indicate that the proposed method outperforms other attack methods.

Security tracking control for discrete-time stochastic systems subject to cyber attacks

This paper is concerned with the security tracking problem under the quadratic cost criterion for a class of discrete-time stochastic linear networked control systems (NCSs) exposed to cyber attacks, covering false data injection attacks as well as a class of DoS attacks. Taking into account factors such as network congestion and the defensive role of intrusion detection systems, successful attack events are modeled as a Bernoulli random sequence. To describe the transient trajectory of an NCS under the impact of a random attack, a probabilistic definition of secure trackability is taken. Therefore, an observer-based dynamic output feedback controller is designed in order to achieve the specified probabilistic secure trackability. Specifically, the probabilistic safety output tracking problem is transformed into an input-to-state stability problem in the probabilistic sense for closed-loop systems with some new sufficient conditions, provided that an augmented incremental model is utilized Then, the controller parameters and the upper bound of the quadratic cost function are determined by solving matrix inequalities, while easy-solution forms of the matrix inequalities to be solved are presented by the Schur complementary lemma. Both simulation studies and practical experiments demonstrate the effectiveness of the proposed control scheme.

Dynamic event-based finite-horizon H∞ secure consensus control of a class of nonlinear multi-agent systems

In this paper, we investigate the H_∞ consensus control issue for nonlinear multi-agent systems (MASs) subject to multiple attacks over a finite time interval. A novel and comprehensive model to characterize the multiple attacks is presented that includes denial-of-service (DoS) attacks, scaling attacks and replay attacks. With the hope of easing the communication burdens, we implement a dynamic event-triggered scheme to schedule the process of data sharing among the individual subsystems, which helps judge if the collected data should be shared to neighboring agents for control input update. The aim of the proposed problem is to develop an output feedback strategy to meet the desired H_∞ consensus performance despite the existence of multiple attacks. Some conditions are presented for the solvability of the investigated problem, and the feedback gains are obtained via certain convex optimization algorithms. The proposed theoretical result is finally demonstrated by virtue of two illustrative simulation examples.

Security correction control of stochastic cyber-physical systems subject to false data injection attacks with heterogeneous effects

In this paper, the interconnected observer intervention-based security correction control idea is proposed for stochastic cyber-physical systems (CPSs) subjected to false data injection attacks (FDIAs). The FDIAs are injected into the controller-to-actuator channel by the adversary via wireless transmission. In particular, the FDIAs with heterogeneous effects are constructed, which consist of periodic attacks with unknown parameters and bias injection attacks with asymptotic convergence property. A novel interconnected adaptive observer structure is designed to online estimate the heterogeneous attack effects. The security correction control scheme with resilience is presented by integrating interconnected adaptive observer and robust technology. It is demonstrated that the impaired state signals can be corrected and desired security performance can be guaranteed for stochastic CPSs under FDIAs with heterogeneous effects. Finally, two simulation verifications, including a F-16 longitudinal dynamics system controlled by network, are established to verify the validity and feasibility for the presented strategy.

Mitigation strategies for communication networks induced impairments in autonomous microgrids control: A review

<abstract> <p>The advancement in communication technology and the availability of intelligent electronic devices (IEDs) have impacted positively on the penetration of renewable energy sources (RES) into the main electricity grid. High penetration of RES also come along with greater demand for more effective control approaches, congestion management techniques, and microgrids optimal dispatch. Most of the secondary control methods of microgrid systems in the autonomous mode require communication links between the distributed generators (DGs) for sharing power information and data for control purposes. This article gives ample review on the communication induced impairments in islanded microgrids. In the review, attention is given to communication induced delay, data packet loss, and cyber-attack that degrades optimal operations of islanded microgrids. The review also considered impairments modelling, the impact of impairments on microgrids operation and management, and the control methods employed in mitigating some of their negative impacts. The paper revealed that innovative control solutions for impairment mitigation rather than the development of new high-speed communication infrastructure should be implemented for microgrid control. It was also pointed out that a sparse communication graph is the basis for communication topology design for distributed secondary control in the microgrid.</p> </abstract>