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Record W4281550050 · doi:10.1155/2022/2189000

A Novel Virtual Voltage Comparison Compensation for Dynamic Voltage Restorer

2022· article· en· W4281550050 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

VenueJournal of Electrical and Computer Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Quality and Harmonics
Canadian institutionsUniversity of Calgary
FundersKey Laboratory of Anhui Province for Testing Technology and Energy-saving DevicesAnhui Polytechnic University
KeywordsVoltage sagCompensation (psychology)VoltageFlexibility (engineering)SIGNAL (programming language)Control theory (sociology)GridComputer scienceVoltage regulationPower qualityEngineeringControl (management)Electrical engineeringMathematics

Abstract

fetched live from OpenAlex

In view of the high-quality requirements of the power supply quality of the user-side sensitive equipment, the system for voltage sag in the grid operation is stable and efficient. This paper proposes a new virtual voltage comparison compensation dynamic voltage recovery method. The algorithm that uses the invariant constant signal as the input quantity, and the real-side input load side signal reduces the influence of the input quantity uncertainty change on the generated compensation signal and improves the simplicity of the system operation control and the system stability. The flexibility of the proposed control strategy, as well as the effectiveness of the optimal design method, is verified by both simulation and experimental results.

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 score0.821
Threshold uncertainty score0.557

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 head0.015
GPT teacher head0.230
Teacher spread0.215 · 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