On PMU Data Integrity Under GPS Spoofing Attacks: A Sparse Error Correction Framework

Silva, Shashini De; Kim, Jinsub; Cotilla‐Sanchez, Eduardo; Hagan, Travis

doi:10.1109/tpwrs.2021.3069219

Cited by 13 publications

(7 citation statements)

References 32 publications

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“…This work demonstrates a practical implementation of the DCL initially presented in [2], which serves as one such countermeasure against GPS spoofing attacks. The authors in [2] assumed only a small fraction of PMUs were under a GPS spoofing attack, and they proposed a sparse error correction formulation to detect and estimate the angle biases introduced by the GPS spoofing attack. The authors also proposed a way to decompose the network into several zones based on network topology and PMU placement and apply the algorithm to each zone separately.…”

Section: B Vulnerability Of Synchrophasor Datamentioning

confidence: 98%

“…With the above motivation in mind, this paper details the development of a Hardware-in-the-Loop (HIL) test system meant to test and verify the functionality of a real-time cybersecurity algorithm known as the Data Correction Library (DCL) [1] that detects and corrects sophisticated synchrophasor measurement spoofing [2]. Synchrophasor data is generated by Phasor Measurement Units (PMUs), which are grid connected devices designed for wide-area monitoring and measurement by employing GPS-based clocks to precisely timestamp data.…”

Section: A Contributions To Hardware-in-the-loop Test Systemsmentioning

confidence: 99%

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Implementing Power System Protection Algorithms in a Digital Hardware-in-the-Loop Substation

Hagan

Senaratne

Meier³

et al. 2023

IEEE Open J. Power Energy

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Real-time power system algorithms are necessary for grid advancement, but few practical applications have been demonstrated in a research usability context. The work in this paper consists of implementing a data correction algorithm, its deployment within realistic substation equipment, and the design of a testbed to demonstrate the overall framework and its usability. A digital simulator is used to generate Phasor Measurement Unit (PMU) data for the algorithm. The algorithm corrects data that has been perturbed by GPS spoofing attacks. Finally, the entire system is visualized on power-utility software, SEL Synchrowave Operations. Considerations and potential issues are discussed and are applicable to digital Hardware-in-the-Loop (HIL) systems as well as to field-deployed systems. The system is demonstrated with 11 simultaneously GPS-spoofing attacked PMUs in a 21 PMU system. The HIL testbed developed in this paper provides a valuable tool for easily testing a variety of real-time power system algorithms and the communications and control necessary for them operate successfully.INDEX TERMS Synchrophasor, phasor measurement unit, GPS spoofing, hardware-in-the-loop, real-time power system simulation.

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Section: B Vulnerability Of Synchrophasor Datamentioning

confidence: 98%

Section: A Contributions To Hardware-in-the-loop Test Systemsmentioning

confidence: 99%

Implementing Power System Protection Algorithms in a Digital Hardware-in-the-Loop Substation

Hagan

Senaratne

Meier³

et al. 2023

IEEE Open J. Power Energy

Self Cite

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“…The goal of launching false data injection attacks is to maximize the state error covariance. This is achieved by solving (5). The optimized value of (3) is: φ D ({0, 0, 0, 1, 0, 0, 1, 0, 0}) = 30.2133.…”

Section: Solution Approachmentioning

confidence: 99%

A Bi-Level Stochastic Game Model for PMU Placement in Power Grid with Cybersecurity Risks

Ghosh¹,

Venkatraman²,

Ahmed³

et al. 2023

Preprint

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Phasor measurement units (PMUs) provide accurate and high-fidelity measurements in order to monitor the state of the power grid and support various control and planning tasks. However, PMUs have a high installation cost prohibiting their massive deployment. Minimizing the number of installed PMUs needs to be achieved while also maintaining full observability of the network. At the same time, data integrity attacks on PMU measurements can cause mislead power system control and operation routines. In this paper, a bi-level stochastic noncooperative game-based placement model is proposed for PMU allocation in the presence of cyber-attack risks. In the first level, the protection of individual PMU placed in a network is addressed, while considering the interaction between the grid operator and the attacker with respective resource constraints. In the second level, the attacker observes the placement of the PMUs and compromises them, with the aim of maximizing the state estimation error and reducing the observability of the network. The proposed technique is deployed in the IEEE-9 bus test system. The results demonstrate a 9% reduction in the cost incurred by the power grid operator for deploying PMUs while considering cyber-risks.

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“…Authors in [24] fuse the data of PMUs, supervisory control and the data acquisition system (SCADA) to detect attacks and correct the data. In [25], a residue formula based on a projection operator is considered to detect suspected PMUs and then, the attacks with a linear least square optimization problem are estimated. Authors in [26] have implemented the idea presented in [25], practically.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [25], a residue formula based on a projection operator is considered to detect suspected PMUs and then, the attacks with a linear least square optimization problem are estimated. Authors in [26] have implemented the idea presented in [25], practically. In order to deal with this type of attacks, an iterative algorithm based on a statistical model is presented in [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

Sabouri

Siamak

Dehghani

et al. 2023

IET Generation Trans & Dist

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The growing use of wireless technologies in power systems has raised concerns about cybersecurity, particularly regarding GPS spoofing attacks (GSAs). These attacks manipulate GPS data, leading to modifications in the phase angle of phasor measurement units (PMUs). In this paper, a Deep‐learning GPS‐Spoofing Counteraction (DLGSC) algorithm is proposed, utilizing PMU data for GSA detection and PMU data correction. The algorithm incorporates a recurrent neural network (RNN) and a set of long short‐term memory (LSTM) units separately, for signal correction after attack detection. Unlike existing methods that struggle with simultaneous attacks or they are static methods, DLGSC tackles these challenges by leveraging deep learning techniques. By selecting appropriate features for GSA detection, DLGSC achieves accurate results. The algorithm is evaluated on standard IEEE 14‐bus and IEEE 39‐bus power systems, and its performance is compared to statistical, dynamic, and Deep Learning (DL) methods in the literature. Additionally, an experimental setup is designed to validate the algorithm in a laboratory environment. Results demonstrate the easy‐implementable DLGSC algorithm's satisfactory real‐time performance in various scenarios, such as load variations and noise, achieving over 98% accuracy. Notably, DLGSC is cable of detecting multiple GSAs on different PMUs.

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On PMU Data Integrity Under GPS Spoofing Attacks: A Sparse Error Correction Framework

Cited by 13 publications

References 32 publications

Implementing Power System Protection Algorithms in a Digital Hardware-in-the-Loop Substation

Implementing Power System Protection Algorithms in a Digital Hardware-in-the-Loop Substation

A Bi-Level Stochastic Game Model for PMU Placement in Power Grid with Cybersecurity Risks

Increasing the resiliency of power systems in presence of GPS spoofing attacks: A data‐driven deep‐learning algorithm

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