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False data injection (FDI) attacks targeting under-load tap changing (ULTC) transformers pose a significant threat to smart distribution networks by exploiting vulnerabilities in the volt-var optimization (VVO) process, leading to potential undervoltage and voltage collapse. The increased integration of renewable energy and cyber-physical systems has expanded the attack surface, making traditional detection methods inadequate. For example, in 2023, attacks on utilities and decentralized components in the United States rose by 200%, with overall cyber threats increasing by 104%, highlighting growing vulnerabilities in distribution systems. To this end, this article proposes a two-stage remediation framework for decentralized FDI (DFDI) attacks targeting ULTC transformers. In the attack stage, vulnerabilities in ULTCs and voltage regulators are scrutinized, risking voltage collapse or blackouts in the distribution system. In the remediation stage, the distribution system operator focuses on non-attacked ULTCs, voltage regulators, distributed generation (DG) units, and smart homes to minimize reliance on compromised components. In this regard, a distinctive formulation of distribution network resilience and load management (DNRLM) problem is introduced to identify a resilient network topology and determine a situational power balance strategy. The proposed framework focuses on minimizing the system's reliance on the attacked ULTCs and voltage regulator components, thereby avoiding the intended voltage collapse caused by such DFDIs. The simulation results verify that the proposed method reduces the voltage collapse proximity index by over 60%, enhancing system resilience under DFDI attacks.
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False Data Injection Cyberattacks Targeting Electric Vehicles in Smart Power Distribution Systems
This paper presents a false data injection (FDI) attack model to target a selection of plugged-in electric vehicles (EVs) in a smart power distribution system resulting in a range of operational issues including but not limited to voltage collapse. To reduce the total cost and difficulty of the cyberattack, attacker utilizes a pre-attack analysis via generating PV and VQ curves for the buses of the distribution system in order to precisely recognize the weakest buses of the system (i.e., the most vulnerable ones) and also the required active and reactive power to be injected into the targeted buses to result in voltage collapse. The effectiveness of the proposed attack model is validated on an IEEE test distribution system modified to contain distributed generation (DG) and EV aggregators.
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- Award ID(s):
- 2348420
- PAR ID:
- 10607942
- Publisher / Repository:
- IEEE Transportation Electrification Conference and Expo (ITEC)
- Date Published:
- ISBN:
- 979-8-3503-1766-4
- Page Range / eLocation ID:
- 1 to 5
- Format(s):
- Medium: X
- Location:
- Chicago, IL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ITEC60657.2024.10599083
