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Resilient Event-Based Fuzzy Fault Detection for DC Microgrids in Finite-Frequency Domain against DoS Attacks.
Ma, Bowen; Lu, Qing; Gu, Zhou.
Afiliación
  • Ma B; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Lu Q; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
  • Gu Z; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel) ; 24(9)2024 Apr 23.
Article en En | MEDLINE | ID: mdl-38732783
ABSTRACT
This paper addresses the problem of fault detection in DC microgrids in the presence of denial-of-service (DoS) attacks. To deal with the nonlinear term in DC microgrids, a Takagi-Sugeno (T-S) model is employed. In contrast to the conventional approach of utilizing current sampling data in the traditional event-triggered mechanism (ETM), a novel integrated ETM employs historical information from measured data. This innovative strategy mitigates the generation of additional triggering packets resulting from random perturbations, thus reducing redundant transmission data. Under the assumption of faults occurring within a finite-frequency domain, a resilient event-based H-/H∞ fault detection filter (FDF) is designed to withstand DoS attacks. The exponential stability conditions are derived in the form of linear matrix inequalities to ensure the performance of fault detected systems. Finally, the simulation results are presented, demonstrating that the designed FDF effectively detects finite-frequency faults in time even under DoS attacks. Furthermore, the FDF exhibits superior fault detection sensitivity compared to the conventional H∞ method, thus confirming the efficacy of the proposed approach. Additionally, it is observed that a trade-off exists between fault detection performance and the data releasing rate (DRR).
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2024 Tipo del documento: Article País de afiliación: China