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Record W3007775060 · doi:10.1049/iet-cps.2019.0032

Cyber–physical attacks on power distribution systems

2020· article· en· W3007775060 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Cyber-Physical Systems Theory & Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsConcordia UniversityYork UniversityUniversity of Waterloo
Fundersnot available
KeywordsCyber-physical systemComputer securityComputer sciencePower (physics)Operating systemPhysics

Abstract

fetched live from OpenAlex

This study investigates the impacts of stealthy false data injection (FDI) attacks that corrupt the state estimation operation of power distribution systems (PDS). In particular, the authors analyse FDI attacks that target the integrity of distribution systems optimal power flow (DSOPF) in order to maximise the system operator losses. The branch current state estimation method is implemented to accurately model the PDS, and convex relaxations are applied to the DSOPF model. The effects of the FDI attacks are analysed on the IEEE 34‐bus unbalanced radial distribution system, with distributed energy resources (DERs) along the feeder. A 24 h DSPOF is performed, and the results depict the changes in the voltage profile and the additional power injection from the DERs, which consequently lead to the increase of the DSOPF cost.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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