Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Zhang, Jinan; Guo, Lulu; Ye, Jin

doi:10.1109/tsg.2021.3121009

Cited by 19 publications

(10 citation statements)

References 36 publications

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“…A dynamic Bayesian network is applied in [87] to detect unobservable attacks; i.e., too many measurements are discarded in the system. In [88], a physics-data-based detection method is proposed to detect the cyber-attack on transformers to ensure solar farm security.…”

Section: Cyber-attacks Detectionmentioning

confidence: 99%

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

Hu,

Yu,

Trinh

2024

Designs

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A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, which is a crucial consideration for the emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication links. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyber-attacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control as well as modern probabilistic and data-driven approaches for modeling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

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Section: Cyber-attacks Detectionmentioning

confidence: 99%

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

Hu,

Yu,

Trinh

2024

Designs

View full text Add to dashboard Cite

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“…Researchers in [86] propose a conditional deep belief network (CDBN) detection strategy that extracts temporal features from the measurements of distributed sensors, which is robust against different environment noise levels. Dynamic Bayesian network is applied in [87] to detect unobservable attacks, i.e., too many measurements are discarded in the system. In [88], a physics-data-based detection method is proposed to detect the cyber-attack on TLP transformers to ensure solar farm security.…”

Section: Cyber Attacks Detectionmentioning

confidence: 99%

A Review of Uncertainties in Power Systems - Modelling, Impact, and Mitigation

Hu,

Yu,

Trinh

2023

Preprint

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A comprehensive review of uncertainties in power systems, covering modeling, impact, and mitigation, is essential to understand and manage the challenges faced by the electric grid. Uncertainties in power systems can arise from various sources and can have significant implications for grid reliability, stability, and economic efficiency. Australia, susceptible to extreme weather such as wildfires and heavy rainfall, faces vulnerabilities in its power network assets. The decentralized distribution of population centers poses economic challenges in supplying power to remote areas, a crucial consideration for emerging technologies emphasized in this paper. In addition, the evolution of modern power grids, facilitated by deploying the advanced metering infrastructure (AMI), has also brought new challenges to the system due to the risk of cyber-attacks via communication link. However, the existing literature lacks a comprehensive review and analysis of uncertainties in modern power systems, encompassing uncertainties related to weather events, cyberattacks, and asset management, as well as the advantages and limitations of various mitigation approaches. To fill this void, this review covers a broad spectrum of uncertainties considering their impacts on the power system and explores conventional robust control, and modern probabilistic and data-driven approaches for modelling and correlating the uncertainty events to the state of the grid for optimal decision making. This article also investigates the development of robust and scenario-based operations, control technologies for microgrids (MGs) and energy storage systems (ESSs), and demand-side frequency control ancillary service (D-FCAS) and reserve provision for frequency regulation to ensure a design of uncertainty-tolerance power system. This review delves into the trade-offs linked with the implementation of mitigation strategies, such as reliability, computational speed, and economic efficiency. It also explores how these strategies may influence the planning and operation of future power grids.

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“…Many countries and regions focus on wind power generation, new energy vehicles, and other aspects. 1–6 As the core component of the above system, the motor has been widely studied, among which, the permanent magnet synchronous motor stands out in a crowd of motors because of its high-power density, high efficiency, lightweight, and other advantages. 7–11…”

Section: Introductionmentioning

confidence: 99%

Simplified robust two-step model predictive control for permanent magnet synchronous motor

Zhang,

Cen

2024

Advances in Mechanical Engineering

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Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.

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Cyber-Attack Detection for Photovoltaic Farms Based on Power-Electronics-Enabled Harmonic State Space Modeling

Cited by 19 publications

References 36 publications

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

A Review of Uncertainties in Power Systems—Modeling, Impact, and Mitigation

A Review of Uncertainties in Power Systems - Modelling, Impact, and Mitigation

Simplified robust two-step model predictive control for permanent magnet synchronous motor

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