Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913643484938240 |
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| author | Zhang, Yi Rajabinezhad, Mohamadamin Wang, Yichao Zhao, Junbo Zuo, Shan |
| author_facet | Zhang, Yi Rajabinezhad, Mohamadamin Wang, Yichao Zhao, Junbo Zuo, Shan |
| contents | This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks. Our framework features a consensus-based secondary control for each converter, effectively addressing these advanced threats. To further safeguard sensitive operational data, a privacy-preserving mechanism is incorporated into the control design, ensuring that critical information remains secure even under adversarial conditions. Rigorous Lyapunov stability analysis confirms the framework's ability to maintain critical DC microgrid operations like voltage regulation and load sharing under EU-FDI threats. The framework's practicality is validated through hardware-in-the-loop experiments, demonstrating its enhanced resilience and robust privacy protection against the complex challenges posed by quick variant FDI attacks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00588 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks Zhang, Yi Rajabinezhad, Mohamadamin Wang, Yichao Zhao, Junbo Zuo, Shan Systems and Control This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks. Our framework features a consensus-based secondary control for each converter, effectively addressing these advanced threats. To further safeguard sensitive operational data, a privacy-preserving mechanism is incorporated into the control design, ensuring that critical information remains secure even under adversarial conditions. Rigorous Lyapunov stability analysis confirms the framework's ability to maintain critical DC microgrid operations like voltage regulation and load sharing under EU-FDI threats. The framework's practicality is validated through hardware-in-the-loop experiments, demonstrating its enhanced resilience and robust privacy protection against the complex challenges posed by quick variant FDI attacks. |
| title | Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2501.00588 |