Robust Distributed Load Frequency Control for Multiarea Wind Energy-Dominated Microgrids Considering Phasor Measurement Unit Failures

Hu, Zhijian; Su, Rong; Wang, Ruiping; Liu, Guangliang; Zhang, Kun; Xie, Xiangpeng

doi:10.1109/jiot.2024.3385992

Cited by 2 publications

References 40 publications

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Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship

Yan,

Wang,

Fan

et al. 2024

Electronics

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The grid interconnections for direct-drive wind farms have triggered multiple new sub-synchronous oscillation events, which can prevent the power system from operating safely and stably. However, the excessively high order of the detailed model for large-scale wind farms with multiple direct-drive permanent magnet synchronous generators (PMSGs) connected to power systems poses a challenge when investigating small disturbance stability and instability mechanisms. This study establishes a model of the grid-connected PMSG system based on the voltage/power excitation–response relationship to describe the dynamic characteristics of the port of the PMSG, and the analysis of active and reactive response characteristics of PMSG lays the foundation for model simplification. Based on the unit model, a direct-drive wind farm aggregation equivalence method based on the excitation–response relationship is proposed. The equivalent model obtained by this method is suitable for the small disturbance stability analysis of direct-drive wind farms grid connected system, with good accuracy. The simulation verified the effectiveness of the aggregation model.

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Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship

Yan,

Wang,

Fan

et al. 2024

Electronics

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Enhancing Smart Grid Data Utilization Within the Internet of Things Paradigm: A Cyber-Physical Security Framework

Hu,

2024

Data Privacy - Techniques, Applications, and Standards [Working Title]

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The integration of Internet of Things (IoT) technologies transforms traditional power systems into smart grids with more opportunities for optimizing power generation and consumption. However, this integration incurs significant cyber-physical security challenges that must be addressed to ensure the authenticity of critical data. This chapter explores the intersection of smart grid data utilization and cyber-physical security within the IoT paradigm. We first introduce the key components of IoT systems and their communication in smart grids, highlighting the interdependencies and vulnerabilities. Then, we discuss the potential risks associated with the collection, transmission, and utilization of data in smart grid environments, emphasizing the importance of cyber-physical security countermeasures in mitigating these risks. Finally, we propose a cyber-physical security framework equipped with dual risk-mitigation layers, including offline parameter configuration and online intrusion detection, to safeguard smart grid data against cyber-physical threats. By adopting this security framework, stakeholders can leverage the full potential of IoT technologies in smart grids while ensuring the security of the critical infrastructure. This chapter contributes to the ongoing discourse on cyber-physical security in smart grids and provides practical insights for policymakers, industry practitioners, and researchers seeking to address the evolving challenges in this domain.

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Robust Distributed Load Frequency Control for Multiarea Wind Energy-Dominated Microgrids Considering Phasor Measurement Unit Failures

Cited by 2 publications

References 40 publications

Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship

Aggregation Equivalence Method for Direct-Drive Wind Farms Based on the Excitation–Response Relationship

Enhancing Smart Grid Data Utilization Within the Internet of Things Paradigm: A Cyber-Physical Security Framework

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