False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters

Tan, Sen; Xie, Peilin; Guerrero, Josep M.; Vasquez, Juan C.

doi:10.1016/j.apenergy.2021.118425

Cited by 38 publications

(16 citation statements)

References 35 publications

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“…MGs heavily depend on telecommunication infrastructure, which brings challenges like connectivity interruptions, leading to potential catastrophic events such as blackouts and partial power outages. To address this, new administrative frameworks have been developed to ensure proper control requirements are met even in cases of connectivity breakdowns [17,18]. Some recent studies [19][20][21] propose innovative auxiliary control mechanisms that reinstate load profiles and frequency stabilization, as well as precise energy allocation based on event-triggered conditions.…”

Section: Introductionmentioning

confidence: 99%

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Ahmed,

Basit,

e Mustafa

et al. 2023

IET Generation Trans & Dist

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Microgrids are a new concept resulting from decentralized power sources, which have changed energy utility grid topography. As this promising field is penetrated, control must be streamlined immediately. This paper analyses key control mechanisms in energy utility networks, including centralized, decentralized, and distributed setups. The focus is on the auxiliary category as the various benefits and drawbacks of each approach is investigated. Information interchange architectures that enable multiple control techniques in the auxiliary control domain are examined using distributed energy networks. The research emphasizes the necessity of auxiliary control in ensuring reliable and effective utility infrastructure operation but notes the lack of a comprehensive scholarly reference. Additionally, the study explores key concerns in utility grids' auxiliary control mechanisms, emphasizing information‐sharing system limits. The goal is to demonstrate the importance of auxiliary control and evaluate its challenges, such as latency, which require new solutions for a seamless transition to a sustainable energy future through rigorous academic assessment. At the auxiliary level, complex, decentralized regulatory mechanisms are recommended to handle transmission channel bandwidth and operational overhead in energy utility networks. These innovative measures have the potential to transform the fundamental structure of our energy landscape, heralding in unprecedented efficiency and sustainability.

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Section: Introductionmentioning

confidence: 99%

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Ahmed,

Basit,

e Mustafa

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

show abstract

“…15,16 Although the data-driven methods are useful in attack detection (especially, these methods do not require accurate models 17,18 ), they may introduce unbearable computational burden, and the requirement of a large amount of training data may not always be satisfied. 9 The knowledge-based detection schemes are alternative methods. A representative strategy is to generate a residual based on state estimation, and by comparing the obtained residual with a properly designed threshold, one can detect whether there happens cyber-attack or not.…”

Section: Introductionmentioning

confidence: 99%

“…The attack detection problem has been considered for various cyber‐attacks on CPSs as in References 8‐11, in which the false data injection attacks (FDIAs) have received increasing attention due to their widely varied types and catastrophic impact 12,13 . As pointed out in Reference 14, existing attack detection methods for revealing FDIAs on CPSs can be roughly classified into data‐driven and knowledge‐based schemes.…”

Section: Introductionmentioning

confidence: 99%

“…The data‐driven detection schemes usually rely on machine learning algorithms or statistical mechanisms to directly infer the system model and detect attacks from the measured data 15,16 . Although the data‐driven methods are useful in attack detection (especially, these methods do not require accurate models 17,18 ), they may introduce unbearable computational burden, and the requirement of a large amount of training data may not always be satisfied 9 . The knowledge‐based detection schemes are alternative methods.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized attack detection for multi‐area power systems via interconnection‐decoupled sliding mode observer

Niu

2023

Intl J Robust & Nonlinear

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This article deals with the false data injection attack (FDIA) detection problem for multi-area power systems. Considering the large-scale characteristic of the multi-area power systems, a novel decentralized detection scheme is proposed to detect FDIAs. First, an unknown input sliding mode observer (UI-SMO) is designed to decouple the interconnected terms, meanwhile, a robust term in the UI-SMO can attenuate the effect of the load variation, by which the obtained residual is only sensitive to local FDIAs. The strict reachability of the sliding surface in the estimate error space is ensured by the robust term with an auxiliary variable, and the stability of the sliding mode dynamics is achieved. Then, a detection logic is delicately designed based on a two-phase residual threshold. Due to the strong attenuating ability to load variation, the proposed detection scheme shows better detectability than some existing works with only decoupling interconnections. Finally, the theoretical results are backed up by a three-area power system simulation with comparison between proposed UI-SMO-based and traditional UIO-based detection scheme.

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“…The distributed control systems' reliance on a global communication network makes the entire system vulnerable to cyber-attacks. This has led to a greater interest in developing and implementing attack detection strategies for DCMGs using distributed control systems; however, significant technical gaps remain [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids

et al. 2022

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This paper proposes a Data-Driven (DD) framework for the real-time monitoring, detection, and mitigation of False Data Injection (FDI) attacks in DC Microgrids (DCMGs). A supervised algorithm is adopted in this framework to continuously estimate the output voltage and current for all Distributed Generators (DGs) with acceptable accuracy. Accordingly, among the various evaluated supervised DD algorithms, Adaptive Neuro-Fuzzy Inference Systems (ANFISs) are utilized because of their low computational burden, efficiency in operation, and simplicity in design and implementation in a distributed control system. The proposed framework is based on the residual analysis of the generated error signal between the estimated and actual sensed signals. The proposed framework detects and mitigates the cyber-attack depending on trends in generated error signals. Moreover, by applying Online Change Point Detection (OCPD), the need for a static user-defined threshold for the residual analysis of the generated error signal is dispelled. Finally, the proposed method is validated in a MATLAB/Simulink testbed, considering the resilience, effectiveness, accuracy, and robustness of multiple case study scenarios.

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False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters

Cited by 38 publications

References 35 publications

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

The nexus of energy in microgrids: A review on communication barriers in distributed networks auxiliary controls

Decentralized attack detection for multi‐area power systems via interconnection‐decoupled sliding mode observer

A Data-Driven Framework for FDI Attack Detection and Mitigation in DC Microgrids

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