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1. A Remedial Action Scheme Against False Data Injection Cyberattacks Targeting ULTC Transformers in Smart Distribution Systems

   https://doi.org/10.1109/ICPSAsia61913.2024.10761531  

   Naderi, Ehsan; Asrari, Arash; Fajri, Poria (July 2024, IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia))

   This paper proposes an on-line remedial action scheme (OLRAS) in order to mitigate the voltage violations caused by false data injection attacks (FDIAs) targeting under load tap changing (ULTC) transformers in smart distribution systems. The FDIA framework contains two different phases. In the attack phase, distribution system operator (DSO), being in attacker's shoe, considers cyberattack scenarios through compromising the results of volt-var optimization problem in a radial distribution grid modified with distributed energy resources (DERs) such as photovoltaic (PV) units and wind turbines (WTs). The outcome of the attack phase will be the compromised voltage profile of the distribution grid showing different rates of voltage violations. In the reaction phase, the DSO rapidly identifies a customized distribution feeder reconfiguration (CDFR) in order to update the flows of active and reactive power throughout the targeted distribution system and recover the voltage profile. The objective functions of the proposed CDFR are defined to minimize the impacts of such cyberattacks targeting ULTCs within distribution grids. This will empower DSOs to react to severe cyberattacks, bypassing the detection stage, and address the voltage violations in a timely manner. The effectiveness of the proposed OLRAS is validated on an IEEE test system. 

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2. A Remediation Framework Against False Data Injection Cyberattacks Targeting ULTC Transformers to Avoid Voltage Collapse in Smart Distribution Networks

   https://doi.org/10.1109/TIA.2025.3542717  

   Naderi, Ehsan; Asrari, Arash (February 2025, IEEE Transactions on Industry Applications)

   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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3. Cyber-Physical Distribution Systems Resilience Against Cyberattacks via a Remediation Framework Based on Static VAR Compensators (SVCs)

   https://doi.org/10.1109/ACCESS.2024.3450631  

   Naderi, Ehsan; Asrari, Arash (August 2024, IEEE Access)

   This paper proposes a framework to optimally employ static VAR compensators (SVCs) within a customized reconfiguration of system topology, leading to remediation of voltage violations caused by false data injection (FDI) cyberattacks targeting smart distribution grids. The designed framework contains formulations associated with planning and operation phases. In the planning phase, the scrutinized system, modified by photovoltaic (PV) units, is enhanced by optimally allocating static VAR compensators (SVCs) to keep the unity power factor throughout the system. Then, distribution system operator (DSO), being in attacker’s shoe, examines relevant cyberattack scenarios leading to voltage violations within the distribution system. Finally, in the operation phase, DSO takes advantage of the optimally planned SVCs to identify proper vectors (i.e., remedial actions) to cope with such potential scenarios of cyberattacks. These (to be recognized) vectors are associated with the variable shunt susceptance of the mentioned SVCs, which will be identified by solving a customized distribution feeder reconfiguration (DFR) problem in the operation phase. The main objective of the customized DFR is to maximize the contributions of SVCs through enhancing the voltage profile of the targeted system. This will enable DSO to mitigate the negative impacts of the FDI attacks and recover the voltage profile of the smart distribution network. The effectiveness of the proposed RAS is validated on three different smart test systems (i.e., 33-bus, 95-bus, and 136-bus systems), which are modified to contain SVC components and renewable-based distributed generation (DG) units. 

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4. Deep Learning Based Multi-Label Attack Detection for Distributed Control of AC Microgrids

   https://doi.org/10.1109/SmartGridComm51999.2021.9631998  

   Mohiuddin, Sheik M.; Qi, Junjian; Fung, Sasha; Huang, Yu; Tang, Yufei (October 2021, 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))

   This paper presents a deep learning based multi-label attack detection approach for the distributed control in AC microgrids. The secondary control of AC microgrids is formulated as a constrained optimization problem with voltage and frequency as control variables which is then solved using a distributed primal-dual gradient algorithm. The normally distributed false data injection (FDI) attacks against the proposed distributed control are then designed for the distributed gener-ator's output voltage and active/reactive power measurements. In order to detect the presence of false measurements, a deep learning based attack detection strategy is further developed. The proposed attack detection is formulated as a multi-label classification problem to capture the inconsistency and co-occurrence dependencies in the power flow measurements due to the presence of FDI attacks. With this multi-label classification scheme, a single model is able to identify the presence of different attacks and load change simultaneously. Two different deep learning techniques are compared to design the attack detector, and the performance of the proposed distributed control and the attack detector is demonstrated through simulations on the modified IEEE 34-bus distribution test system. 

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5. False Data Injection Cyberattacks Targeting Electric Vehicles in Smart Power Distribution Systems

   https://doi.org/10.1109/ITEC60657.2024.10599083  

   Naderi, Ehsan; Asrari, Arash; Fajri, Poria (June 2024, IEEE Transportation Electrification Conference and Expo (ITEC))

   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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