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Record W2787104104 · doi:10.1109/pesgm.2017.8274452

Energy storage system control for prevention of transient under-frequency load shedding

2017· article· en· W2787104104 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransient (computer programming)Control theory (sociology)Electric power systemVoltage droopLoad SheddingController (irrigation)InertiaReliability (semiconductor)EngineeringAutomatic frequency controlEnergy storageComputer scienceKalman filterControl engineeringPower (physics)Control (management)VoltageVoltage regulator

Abstract

fetched live from OpenAlex

This paper proposes and evaluates a systematic method for controlling an energy storage system for preventing load shedding due to transient declines in frequency. The proposed controller works on local measurements and uses an extended Kalman filter to perform online identification of system parameters. These results are then used to implement a model predictive controller for the safe withdrawal of support and energy recovery, both without incurring transient load shedding. The formulation accounts for single generator outages and includes power system parameters such as inertia, damping, and droop. The proposed method is evaluated using the nonlinear simulations of 6-bus and 24-bus test systems known as Roy Billinton Test System (RBTS) and IEEE Reliability Test System. The results show that the proposed controller is effective in preventing transient load shedding.

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 score0.993
Threshold uncertainty score0.333

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 head0.014
GPT teacher head0.236
Teacher spread0.222 · 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

Quick stats

Citations29
Published2017
Admission routes1
Has abstractyes

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