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Record W2014113518 · doi:10.1109/ccece.2010.5575228

Impact of maximum power point tracking on grid-connected Photovoltaic system dynamics

2010· article· en· W2014113518 on OpenAlexaff
Prajna Paramita Dash, Mehrdad Kazerani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaximum power point trackingControl theory (sociology)Photovoltaic systemCompensation (psychology)Maximum power principleSteady state (chemistry)VoltageOperating pointPower (physics)GridNonlinear systemLoop (graph theory)Computer scienceEngineeringElectronic engineeringPhysicsControl (management)MathematicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper studies the performance of a single-stage, three-phase Photovoltaic (PV) system that is connected to a distribution network. The control is based on an outer voltage control loop and an inner current regulation loop. A Maximum Power Point Tracker (MPPT) based on Perturb & Observe (P&O) method is interfaced to outer voltage control loop. The outer voltage control loop is based on feed-forward compensation strategy to make the PV system dynamics immune to the PV array nonlinear characteristics. Through simulation and mathematical analysis, it has been shown in previous research work that in the absence of feed-forward compensation change in the operating condition causes oscillation with weather condition unchanged. This paper shows through simulation that incorporation of MPPT in the PV system under steady-state renders an optimal operating point which results in a stable operation irrespective of weather condition and without feed-forward compensation. Moreover, an eigen value analysis is carried out both with the MPPT and without the MPPT to verify the simulated results.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.008
GPT teacher head0.253
Teacher spread0.245 · 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.

Study designBench or experimental
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

Citations0
Published2010
Admission routes1
Has abstractyes

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