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Record W3196682405 · doi:10.23977/jeeem.2021.040103

Summary of Research on Intelligent Control algorithm and Application of Power system Automation

2021· article· en· W3196682405 on OpenAlexvenueno aff
Zixuan Yu, Mengshi Li

Bibliographic record

VenueJournal of Electrotechnology Electrical Engineering and Management · 2021
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsAutomationIntelligent controlIntellectualizationComputer scienceArtificial neural networkFutures studiesField (mathematics)Fuzzy control systemControl engineeringControl (management)Automatic controlFuzzy logicEngineeringAlgorithmArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

With the development of HVDC technology and the continuous progress of AC/DC hybrid power grid, the research of intelligent control in the direction of power system automation has made some progress and has a certain foresight. The power system automatic intelligent control technology algorithm discussed in this paper includes neural network, fuzzy control algorithm and genetic algorithm, application development and significance, through the development of related intelligent algorithms to provide some reference for the field.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2021
Admission routes1
Has abstractyes

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