MétaCan
Menu
Back to cohort
Record W2765860318 · doi:10.1115/1.4038342

The Coordinated Immersion and Variance Control of Power Systems With Excitation and Steam-Valve

2017· article· en· W2765860318 on OpenAlexaff
Shengtao Li, Xiaomei Liu, Xiaoping Liu

Bibliographic record

VenueJournal of Dynamic Systems Measurement and Control · 2017
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsLakehead University
FundersDistinguished Middle-Aged and Young Scientist Encourage and Reward Foundation of Shandong ProvinceNational Natural Science Foundation of ChinaMinistry of Education of the People's Republic of ChinaNational Science Foundation
KeywordsControl theory (sociology)Lyapunov functionNonlinear systemElectric power systemBoiler (water heating)Controller (irrigation)Transient (computer programming)Computer scienceControl engineeringEngineeringPower (physics)Control (management)Physics

Abstract

fetched live from OpenAlex

Transient stability is the key problem for reliable and secure planning under the new deregulated market conditions. By using immersion and invariance (I&I) method, a nonlinear coordinated generator excitation and steam-valve controller is designed to improve transient stability of power systems. The proposed coordinated I&I controller can assure power angle stability, voltage, and frequency regulations, when a large disturbance occurs on the transmission line or a small perturbation to mechanical power. Compared with the Lyapunov method, the proposed method does not need to construct a Lyapunov energy function. Some numerical simulations are used to validate the proposed controller. Simulation results show that the nonlinear coordinated I&I controller has better control performance than the existing coordinated passivation controller (CPC).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Dynamic Systems Measurement and ControlSame topicPower System Optimization and StabilityFrench-language works237,207