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Record W2920986600 · doi:10.1109/epetsg.2018.8658853

Virtual Synchronous Generator with Variable Inertia Emulation Via Power Tracking Algorithm

2018· article· en· W2920986600 on OpenAlexaff
Rama Krishna Naidu Vaddipalli, Luiz A. C. Lopes, Akshay Kumar Rathore

Bibliographic record

Venue2018 2nd International Conference on Power, Energy and Environment: Towards Smart Technology (ICEPE) · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsConcordia University
Fundersnot available
KeywordsControl theory (sociology)Voltage droopInertiaPermanent magnet synchronous generatorMaximum power point trackingPhase-locked loopComputer scienceAC powerEmulationVoltage sourcePower (physics)VoltageEngineeringElectronic engineeringElectrical engineeringJitterControl (management)

Abstract

fetched live from OpenAlex

This paper proposes the technique, variable inertia emulation of the virtual synchronous generator (VSG) fed from a photo voltaic (PV) source via power tracking algorithm. The virtual synchronous generator is tied to a three-phase distribution line at the point of common coupling (PCC). The operating point on the power - voltage characteristics of the PV source is suitability controlled to emulate the inertia in the input. The variable inertia is achieved through variation of inertia constant in the closed loop power control. This method of moving on the power - voltage characteristics requires a power tracking algorithm for the PV Source. The VSG is modeled using synchronous reference frame theory with a voltage oriented control scheme(VOC). We have chosen an improved version of phase locked loop(PLL) for the accurate estimation of the phase and extraction of the fundamental component from the voltage at PCC. The droop control characteristics determine the real power and reactive power to be supplied by the VSG. The simulation results support the analysis presented in this paper.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.185
Teacher spread0.179 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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