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Record W2081227144 · doi:10.1007/s40565-014-0079-6

Frequency regulation by fuzzy and binary control in a hybrid islanded microgrid

2014· article· en· W2081227144 on OpenAlexaff
Pamela Astrid Manjarres, O.P. Malik

Bibliographic record

VenueJournal of Modern Power Systems and Clean Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrogridDiesel generatorAutomatic frequency controlFrequency regulationController (irrigation)Control theory (sociology)Control engineeringEngineeringGenerator (circuit theory)Automatic Generation ControlFuzzy logicControl (management)Renewable energyWind powerComputer sciencePower (physics)Electric power systemDiesel fuelAutomotive engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Islanded microgrids must be self-sufficient in terms of frequency and voltage control due to their islanded operation. A control strategy for frequency regulation by combining the operation of a wind generator, a diesel generator, a battery energy storage system and a dump load in a microgrid is proposed in this paper. In the proposed strategy, the control task is partitioned into two subtasks: 1) choosing the appropriate element to be used for regulation, and 2) providing frequency regulation. A global controller chooses the element to operate. Then, the frequency regulation is provided by separate individual controllers. The proposed control strategy is tested on a microgrid with mixed types of generation and modeled on Simulink. By monitoring the power of individual elements and system frequency, it is shown that the proposed control strategy operates efficiently. The proposed strategy facilitates the integration of renewable energy sources and enhances frequency regulation.

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)0.000
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0000.000
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 head0.002
GPT teacher head0.154
Teacher spread0.152 · 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

Citations19
Published2014
Admission routes1
Has abstractyes

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