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Record W3135345040 · doi:10.1049/iet-gtd.2020.0289

Local blackout and global power system wide blackout are caused by non‐linear negative damping

2020· article· en· W3135345040 on OpenAlexafffund
B.T. Ooi, Jinpeng Guo, Xiaozhe Wang

Bibliographic record

VenueIET Generation Transmission & Distribution · 2020
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBlackoutControl theory (sociology)Transient (computer programming)Electric power systemLinearityEngineeringPower (physics)PhysicsComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Local blackout occurs when the transient stability limit is exceeded after a transmission line fault. As to global blackout, a recording of the 10 August 1996‐WECC blackout shows that it was accompanied negative damping. A turbine‐generator swinging against an infinite bus is used to approximate the dynamics leading to the WECC blackout. There is no analytic solution when non‐linearity is included. Small‐signal linearisation shows that negative damping can occur but cannot produce the waveform recorded in the WECC blackout. Therefore, the graphical phase‐plane method, which is easy to use, is resorted to. The study shows that the kernel of non‐linearity consists of the gradients which produce limit cycles. Positive or negative damping is produced by shifting the gradients of the limit cycles to the right or left in the direction of the trajectory. The study makes a contribution by showing that phase plane can be used to study the impact of multiple independent controllers. A worked example shows when transient stability limit is exceeded, local blackout occurs. The swing equation of local blackout is applied to the WECC blackout. As proof that negative damping was due to non‐linearity, a waveform having the frequency of the recorded WECC blackout is presented.

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 categoriesMeta-epidemiology (narrow)
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 score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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.

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

Citations1
Published2020
Admission routes2
Has abstractyes

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