Decentralized Anomaly Characterization Certificates in Cyber-Physical Power Electronics Based Power Systems

As the research of HCPS in EI is ongoing, an increasing number of problems should be addressed, such as the leading role or aid function definition of humans, the coordination of humans with cyber and physical equipment, the state awareness of information handling, etc. Therefore, an increasing number of advanced techniques should be considered and adopted, such as IOT, AI and big data, etc., the main target of which is to improve the timely, digitalized, informationized and intelligent characters of the related tasks. This paper first introduces the applications of CPS in every industry, especially in the power grid and EI (Energy Internet), the introduction of which can be classified as a framework design, support technologies and algorithms, system function, power ancillary service, Energy Internet realization, etc. Then, this paper summarizes the related technologies of Human-Cyber-Physical Systems, which mainly include the areas of system design, theoretical analysis, technique development and application forecasting, which emphasizes its applications in EI. For the development of HCPS, it uses the human-in-the-loop concept and digital transformation as the future research and development directions, the performance of which can be largely promoted through these means. With the aid of HCPS, the optimal system performance of EI can be achieved more robustly, which finally evolves into an integrated and comprehensive modern power grid.

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“…Set up dynamic closed loop fusion model. [23] Decide the boundary of the plane to distinguish grid fault and information attack based on CPS.…”

Section: Undertook Intelligent Collaboration In Cpsmentioning

confidence: 99%

Human-Cyber-Physical Systems for Energy Internet—A Review

et al. 2023

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“…where, K i pv and K i iv are the proportional and integral gains corresponding to the secondary controller. Further, the equation for secondary cooperative controller is represented by (3), The average voltage of the i th agent ( V i (t)) is shown by (4).…”

Section: B Cooperative Secondary Controlmentioning

confidence: 99%

Decentralized Anomaly Identification in Cyber-Physical DC Microgrids

Gupta

Sahoo

Mohanty

et al. 2022

2022 IEEE Energy Conversion Congress and Exposition (ECCE)

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DC microgrids with distributed control architectures enhance the operational reliability, scalability and flexibility. However, the underlying communication infrastucture makes the system highly susceptible to cyber attacks. These attacks in DC microgrids cause severe impact, that can be easily misinterpreted as faults, which can then maloperate the protection decision. Although various protection schemes have been established, a tailor-made scheme to distinguish faults from cyber attacks is needed to ensure reliability of supply. In this paper, we use a two dimensional plane with deviation of current (δI) and voltage (δV ) at the terminal of each converter to distinguish between cyber attacks and faults in DC microgrids. As this scheme is governed based on physics of secondary controller operation, it is simple to implement and scalable to any physical topology. The performance of the proposed scheme is tested with real time simulation in OPAL-RT environment with HYPERSIM software for different topologies including radial, ring and mesh networks. In addition, the scheme is also tested and verified for simultaneous cyber attack on multiple converters. The simulation results validates that the proposed decentralized scheme is effective in both detecting and localizing cyber-physical anomalies within 2 ms.

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“…Also, state estimation methods also require prior knowledge about the physical structure of the system. Physicsinformed anomaly detection techniques have been proposed in [20], [21], which are particularly focused on distinguishing between large signal disturbances, such as grid/sensor faults and cyber-attacks.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

Takiddin

Rath

Ismail

et al. 2022

IEEE Systems Journal

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Cyber-physical systems like microgrids contain numerous attack surfaces in communication links, sensors, and actuators forms. Manipulating the communication links and sensors is done to inject anomalous data that can be transmitted through the cyber-layer along with the original data stream. The presence of malicious, anomalous data packets in the cyber-layer of a DC microgrid can create hindrances in fulfilling the control objectives, leading to voltage instability and affecting load dispatch patterns. Hence, detecting anomalous data is essential for the restoration of system stability. This paper answers two important research questions: Which datadriven detection scheme offers the best detection performance against stealth cyber-attacks in DC microgrids? What is the detection performance improvement when fusing two features (i.e., current and voltage data) for training compared with using a single feature (i.e., current)? Our investigations revealed that (i) adopting an unsupervised deep recurrent autoencoder anomaly detection scheme in DC microgrids offers superior detection performance compared with other benchmarks. The autoencoder is trained on benign data generated from a multi-source DC microgrid model. (ii) Fusing current and voltage data for training offers a 14.7% improvement. The efficacy of the results is verified using experimental data collected from a DC microgrid testbed when subjected to stealth cyber-attacks.

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Decentralized Anomaly Characterization Certificates in Cyber-Physical Power Electronics Based Power Systems

Cited by 6 publications

References 16 publications

Human-Cyber-Physical Systems for Energy Internet—A Review

Human-Cyber-Physical Systems for Energy Internet—A Review

Decentralized Anomaly Identification in Cyber-Physical DC Microgrids

Data-Driven Detection of Stealth Cyber-Attacks in DC Microgrids

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