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Record W2967164492 · doi:10.1109/isgt.2019.8791582

Cyber-Physical Security of State Estimation Against Attacks on Wide-Area Load Shedding Protection Schemes

2019· article· en· W2967164492 on OpenAlexaff
Abdelrahman Ayad, Mohsen Khalaf, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Waterloo
FundersKhalifa University of Science, Technology and ResearchUtah Agricultural Experiment Station
KeywordsLoad SheddingElectric power systemComputer scienceComputer securityPower-system protectionPower (physics)EngineeringReliability engineering

Abstract

fetched live from OpenAlex

Wide-Area Load Shedding (WALS) protection schemes are the schemes used in power system to perform a load shedding after a large disturbance in the power system. These schemes are used to avoid power system cascading if all other remedial means fail to mitigate against a large disturbance. Hence, any small mistake in the operation of WALS protection schemes may result in huge damage in the power system i.e., blackouts, component damage, service disruption, etc. Since WALS protection schemes are centralized schemes that use communication system, they are vulnerable against cyber attacks. This paper shows the effects of False Data Injection (FDI) cyber attacks on the WALS protection schemes. To validate the problem formulation, the IEEE 39 bus New England system is simulated under FDI attacks using the AC power flow. The results show that the attacker can alter the load shedding distribution or location, and as result forces the control center operator to make an unintended load shedding decision and deviate from the optimal course of action.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.218
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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