Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure

One of the intrinsic properties of Distribution networks, resilience, is the ability to resist, adjust, and recover from extreme, high-impact, low-probability events such as earthquakes, floods, hurricanes, thunderstorms, and cyber and physical attacks. Besides, the uncertainty of the network elements has a significant effect on the operation of the distribution system. Operators require methods and planning strategies to improve grid resilience. Distribution network reconfiguration (DNR) enhances reliability and reduces power losses. This paper proposes an application of DNR as a strategy to get a resilient configuration against false data injection (FDI) attack during state estimation (SE) procedure, minimize power losses, and improve the reliability of the distribution network simultaneously. In this paper, a driving training-based optimization (DTBO) method is exploited for DNR to demonstrate the effectiveness of the proposed strategy. The proposed strategy is tested on IEEE 33-bus, 69-bus, and 118-bus systems to reduce FDI attack impact on power measurements, power loss, and energy not supplied (ENS). The proposed DNR is evaluated by offline digital time-domain simulations on the distribution test systems in the MATLAB software environment. The simulations and comparisons of the proposed DNR strategy effectively prove the proposed strategy's effectiveness, accuracy, and authenticity.

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Publication Details

Journal
IEEE Transactions on Network Science and Engineering
Published
2025-02-20
DOI
https://doi.org/10.1109/tnse.2025.3542632
Citations
10
Primary Topic
Smart Grid Security and Resilience
Type
article
Field-Weighted Citation Impact
12.15

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article

Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure

Hamid Reza Baghaee, Gevork B. Gharehpetian, Argo Rosin, Roya Ahmadiahangar et al.
10 citations
IEEE Transactions on Network Science and Engineering
Smart Grid Security and Resilience
12.15
article

Resilient Reliability/Loss-Based Distribution Network Reconfiguration: A Strategy Against FDI Attacks During State Estimation Procedure

Hamid Reza Baghaee, Gevork B. Gharehpetian, Argo Rosin, Roya Ahmadiahangar, Reza Behnam
article en
10 citations

Abstract

One of the intrinsic properties of Distribution networks, resilience, is the ability to resist, adjust, and recover from extreme, high-impact, low-probability events such as earthquakes, floods, hurricanes, thunderstorms, and cyber and physical attacks. Besides, the uncertainty of the network elements has a significant effect on the operation of the distribution system. Operators require methods and planning strategies to improve grid resilience. Distribution network reconfiguration (DNR) enhances reliability and reduces power losses. This paper proposes an application of DNR as a strategy to get a resilient configuration against false data injection (FDI) attack during state estimation (SE) procedure, minimize power losses, and improve the reliability of the distribution network simultaneously. In this paper, a driving training-based optimization (DTBO) method is exploited for DNR to demonstrate the effectiveness of the proposed strategy. The proposed strategy is tested on IEEE 33-bus, 69-bus, and 118-bus systems to reduce FDI attack impact on power measurements, power loss, and energy not supplied (ENS). The proposed DNR is evaluated by offline digital time-domain simulations on the distribution test systems in the MATLAB software environment. The simulations and comparisons of the proposed DNR strategy effectively prove the proposed strategy's effectiveness, accuracy, and authenticity.

IEEE Transactions on Network Science and EngineeringVol. 12(3)
Tallinn University of Technology (EE), Tarbiat Modares University (IR), Amirkabir University of Technology (IR)
Iran National Science Foundation
Climate action
Openalex Percentile: Top 1%
Smart Grid Security and Resilience
12.15
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