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Record W2013312838 · doi:10.1109/pesgm.2014.6938964

Integrating hybrid power source into an islanded MV microgrid using CHB multilevel inverter under unbalanced and nonlinear load conditions

2014· article· en· W2013312838 on OpenAlexaff
Mohsen Hamzeh, Amin Ghazanfari, Hossein Mokhtari, Houshang Karimi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsPolytechnique MontréalUniversity of Alberta
Fundersnot available
KeywordsMicrogridController (irrigation)Control theory (sociology)Photovoltaic systemVoltagePower (physics)InverterEngineeringComputer scienceVoltage sourceElectronic engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper presents a control strategy for an islanded medium voltage (MV) microgrid to coordinate hybrid power source (HPS) units and to control interfaced multilevel inverters under unbalanced and nonlinear load conditions. The proposed HPS systems are connected to the loads through a cascaded H-bridge (CHB) multilevel inverter. The CHB multilevel inverters increase the output voltage level and enhance power quality. The HPS employs fuel cell (FC) and photovoltaic (PV) sources as the main and supercapacitors (SCs) as the complementary power sources. Fast transient response, high performance, high power density, and low FC fuel consumption are the main advantages of the proposed HPS system. The proposed control strategy consists of a power management unit for the HPS system and a voltage controller for the CHB multilevel inverter. Each distributed generation (DG) unit employs a multi proportional resonant (multi-PR) controller to regulate the buses voltages even when the loads are unbalanced and/or nonlinear. Digital time-domain simulation studies are carried out in the PSCAD/EMTDC environment to verify the performance of the overall proposed control system.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.779

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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
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

Citations7
Published2014
Admission routes1
Has abstractyes

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