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Record W2586222769 · doi:10.1109/tsg.2017.2666046

A Class of Switching Exploits Based on Inter-Area Oscillations

2017· article· en· W2586222769 on OpenAlexafffund
Ahmed Khalil, Abdallah Farraj, Deepa Kundur, Reza Iravani

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2017
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectric power systemOscillation (cell signaling)Power (physics)Mode (computer interface)ObservableClass (philosophy)Computer scienceTransmission (telecommunications)ExploitElectronic engineeringControl theory (sociology)EngineeringTopology (electrical circuits)Electrical engineeringTelecommunicationsPhysicsControl (management)

Abstract

fetched live from OpenAlex

This work presents a new class of cyber-physical switching attacks that targets power transmission systems. The proposed approach relies on exciting inter-area oscillation modes in a coordinated manner to drive groups of system generators out of step. Our paradigm targets inter-area oscillations by switching a relatively small part (in the order of 2%) of the system load at a (low) frequency that resonates with one of the inter-area oscillation modes observed in the power system. The switching frequency of the targeted mode is chosen through measurement-based analysis of the frequency deviation at a select bus that is observable by the adversary. The inter-area switching attack is implemented as single-load switching and coordinated multi-load switching and is studied with a variety of switching signals. Numerical results show the potential and characteristics of the proposed switching exploitation when applied to the four-machine two-area power system and the Northeast Power Coordinating Council 68-bus system.

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: none
Teacher disagreement score0.638
Threshold uncertainty score0.733

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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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

Citations36
Published2017
Admission routes2
Has abstractyes

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