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A Battery Storage System for PMG-based WECSs

2020· article· en· W3080335981 on OpenAlexaff
X. F. St. Onge, S. A. Saleh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceRenewable energyEnergy storageWind powerElectric power systemOffset (computer science)Electrical engineeringGenerator (circuit theory)TorqueBattery (electricity)Power (physics)Automotive engineeringEngineering

Abstract

fetched live from OpenAlex

Conventional approaches to electric power generation has become concerning for the environment. As a result, several changes to power systems have been introduced. Among these changes, a significant number of distributed generation units (DGUs) are being integrated in power systems. For, DGUs strive to offset conventional generation by utilizing renewable energy sources. Largely, DGUs have trended towards the use of wind energy conversion systems (WECSs), where modern WECSs are favoring the variable speed PMG-based architectures. The relative novelty of these systems, however, has emphasized challenges that limit their applicability. Of these challenges, PMG torque pulsations, and PCC frequency have been regarded as the most troublesome. As of late, energy storage systems (ESSs) and new power electronic converter (PEC) topologies are being recommended to overcome these challenges. This work aims to develop and evaluate a split-bus PMG-based WECS architecture, that takes advantage of a generator charged and PCC discharged battery storage system (BSS) to support PCC frequency stability, as well as using a modified cascaded H-bridge (MCHB) generator-side PEC to reduce PMG-torque pulsations. The developed system is modeled in simulation and constructed in laboratory. Several operating conditions of wind speed, power command, and BSS charging are investigated.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.231

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 head0.008
GPT teacher head0.163
Teacher spread0.155 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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