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Study on Frequency Optimal Control of Receiving-End Grid Based on Pumped Storage Particle Swarm Algorithm

2022· article· en· W4376457772 on OpenAlexaff
Yuchen Zhao, Zhihui Liu, Xuechen Bai, Jianxiong Jia, Boyu Zhou, Kai Yuan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAutomatic frequency controlFrequency gridGridStability (learning theory)Particle swarm optimizationRenewable energyEnergy storageComputer scienceFrequency regulationPower (physics)Power gridElectric power systemFrequency responseControl theory (sociology)Control (management)EngineeringAlgorithmElectrical engineeringMathematicsTelecommunications

Abstract

fetched live from OpenAlex

In order to ensure that the recipient grid fed by large-capacity and volatile renewable energy can maintain the system frequency stability and prevent frequency collapse, this paper establishes a mathematical model of pumped storage condition conversion, and combines the theory of condition conversion of pumped storage power plant units and the frequency response characteristics of the recipient grid to establish an optimization model for frequency stability control of the recipient grid. Based on the fact that the pumped storage support can improve the frequency stability performance of the receiving-end grid, a simulation model is established to verify the effectiveness of the model in this paper, taking an actual receiving-end grid as an example.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.233
Teacher spread0.219 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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