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Dynamic Phasor Modeling and Control of a Single-Phase Single-Stage Grid-Connected PV System

2021· article· en· W3213277606 on OpenAlexafffund
Udoka C. Nwaneto, Andrew M. Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhasorGridControl theory (sociology)Computer scienceStage (stratigraphy)Single phasePhotovoltaic systemControl (management)Electric power systemEngineeringPhysicsElectrical engineeringMathematicsPower (physics)

Abstract

fetched live from OpenAlex

The increasing deployment of inverter-based sources in power systems and microgrids, and the concomitant reduction in system inertia, have made the study of system dynamic interactions and stability, essential. Detailed inverter models are accurate but computationally inefficient for such studies. This paper presents a dynamic phasor (DP) based model of a single-phase single-stage photovoltaic (PV) inverter. Compared to the existing DP-based PV inverter models in literature, the proposed DP-based PV inverter model includes the PV model and maximum power point tracking (MPPT) dynamics. The DP-based PV inverter model is then linearized around an equilibrium point and the resulting small-signal model is used to develop equations required for obtaining suitable control gains. The DP-based model is built on MATLAB/Simulink and then validated against a detailed model developed in Simulink/Sim-scape. Simulation results confirm the high efficacy of the DP model in predicting dominant harmonics in the AC/DC sides of the inverter, using a simulation step size 50 times larger than the detailed model’s step size. The DP-based PV inverter model is well-suited for system-level study of inverter-based microgrids.

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: Empirical · Consensus signal: none
Teacher disagreement score0.741
Threshold uncertainty score0.644

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 head0.009
GPT teacher head0.199
Teacher spread0.190 · 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

Citations5
Published2021
Admission routes2
Has abstractyes

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