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Record W2127324982 · doi:10.1109/pes.2008.4595978

Control of an islanded Distributed Energy Resource unit with load compensating feed-forward

2008· article· en· W2127324982 on OpenAlexaff
Amirnaser Yazdani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsDispatchable generationAutomatic frequency controlDistributed generationController (irrigation)Compensation (psychology)Control theory (sociology)VoltageComputer scienceVoltage controllerDigital controlVoltage sourceEnergy storageEngineeringElectronic engineeringVoltage droopControl (management)Electrical engineeringPower (physics)Renewable energy

Abstract

fetched live from OpenAlex

This paper develops a voltage/frequency regulation scheme for an islanded distributed energy resource (DER) unit. The DER can be either a distributed storage (DS) unit or a dispatchable distributed generation (DG) unit. The DER unit is represented by a (time-varying) DC voltage source that is electronically interfaced to an aggregate of loads. The electronic interface consists of a voltage-sourced converter (VSC) and an LC filter. First, a dynamic model is developed for the DER unit and the loads. Then, based on the dynamic model, a control scheme is designed to regulate the voltage and frequency of the loads. To mitigate the impact of the loads configurations and dynamic properties on the stability and performance of the closed-loop system, a feed-forward compensation has been incorporated into the design. Performance of the DER unit in conjunction with the voltage/frequency controller is evaluated based on digital time-domain simulation studies in the PSCAD/EMTDC software environment. The simulation results verify the effectiveness of the developed control strategy and feed-forward compensation technique.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.164
Teacher spread0.159 · 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

Citations56
Published2008
Admission routes1
Has abstractyes

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