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Record W2795198686 · doi:10.1109/tec.2018.2819982

A Power Mismatch Elimination Strategy for an MMC-Based Photovoltaic System

2018· article· en· W2795198686 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2018
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan UniversityWestern University
Fundersnot available
KeywordsPhotovoltaic systemMaximum power point trackingModular designPower (physics)Grid-connected photovoltaic power systemComputer scienceAC powerVoltageGridControl theory (sociology)Maximum power principleElectronic engineeringEngineeringElectrical engineeringInverterPhysicsMathematics

Abstract

fetched live from OpenAlex

This paper proposes a power mismatch elimination strategy for a medium-voltage modular multilevel converter (MMC)-based photovoltaic (PV) system. In the MMC-based PV system, each submodule of the MMC is energized by multiple PV generators, each interfaced with the dc port of the submodule by a corresponding isolated dual active bridge dc-dc converter. This configuration allows for a transformerless connection to the host grid and independent maximum power point tracking for the PV generators. The paper then proposes a power mismatch elimination strategy that ensures that the current delivered to the host grid is balanced in spite of unbalanced PV generator outputs. The proposed power mismatch elimination strategy employs a dc differential current to equalize the leg powers, and an ac differential current to stabilize the dc voltages of the submodules. The effectiveness of the proposed power mismatch elimination strategy is demonstrated by time-domain simulations conducted on a model of the PV system in PSCAD/EMTDC software environment.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.790

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 head0.015
GPT teacher head0.227
Teacher spread0.212 · 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