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Record W1982200682 · doi:10.1109/tpwrs.2011.2160100

Online Set Point Adjustment for Trajectory Shaping in Microgrid Applications

2011· article· en· W1982200682 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

VenueIEEE Transactions on Power Systems · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrogridTrajectorySet (abstract data type)Variable (mathematics)Control theory (sociology)Point (geometry)Computer scienceSet pointControl engineeringEngineeringControl (management)MathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes and evaluates a strategy to mitigate the transients of an apparatus, e.g., an electronically-interfaced distributed energy resource (DER) unit. This strategy is based on response monitoring and can be implemented based on either set point automatic adjustment (SPAA) or set point automatic adjustment with correction-enabled (SPAACE). SPAA takes advantage of an approximate model of the system to calculate the intermediate set points. SPAACE monitors the trajectory of the variable of interest and bases its decisions on the trend of variations of that variable and accounts for the inaccuracies and unmodeled dynamics by switching the command input between alternative set points. Case studies are presented to demonstrate the application and effectiveness of the proposed strategy in limiting voltage or current excursions in balanced and unbalanced microgrid study systems, subsequent to a step change in set point, load switching, and black start.

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 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 score0.994
Threshold uncertainty score0.751

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 head0.029
GPT teacher head0.224
Teacher spread0.195 · 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