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Record W3031903866 · doi:10.1109/access.2020.2997291

Online Model-Free Estimation of the Dynamic System Model for a Power System With Renewables in Ambient Conditions

2020· article· en· W3031903866 on OpenAlexafffund
Jinpeng Guo, Xiaozhe Wang, Boon‐Teck Ooi

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRenewable energyNetwork topologyElectric power systemTopology (electrical circuits)Power (physics)Online modelDistributed computingEngineeringElectrical engineeringComputer networkMathematics

Abstract

fetched live from OpenAlex

In this paper, an online model-free method is proposed to estimate the dynamic model of a power system with distributed renewable energy sources (RES). The proposed method, requiring no knowledge of network topology and model parameters, is computationally efficient and can be implemented online. The impact of missing PMUs is evaluated in detail, which shows that most entries of the dynamic model can still be well estimated in the case of missing PMUs. Simulation studies in the modified IEEE 68-bus system demonstrate that the proposed method can provide accurate estimation of the dynamic model in different operating conditions. The method can help detect topology change even when some PMUs are lost.

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 score0.807
Threshold uncertainty score0.371

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 head0.258
Teacher spread0.236 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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