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Record W2611672969 · doi:10.1109/pedes.2016.7914261

Stability analysis of three-loop control for three phase voltage source inverter interfacing to the grid based on state variable estimation

2016· article· en· W2611672969 on OpenAlexfundno aff
Deepthi L. Sivadas, Krishna Vasudevan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
FundersPartenariat Canadien Contre Le Cancer
KeywordsControl theory (sociology)InterfacingRobustness (evolution)GridMATLABParametric statisticsComputer scienceTotal harmonic distortionFrequency gridRobust controlLoop (graph theory)Inner loopControl systemControl engineeringEngineeringVoltageController (irrigation)Control (management)Mathematics

Abstract

fetched live from OpenAlex

Grid tied inverters and associated control techniques have gained importance in the domain of distributed generation. Different methods of control have been compared in literature depending upon the ability to meet THD limits, damping offered to resonant oscillations and stable operation. Multiple loop methods compared to their single-loop counterparts have proved to be highly effective for meeting the grid regulations. This paper shows that amongst multi loop topologies, a three-loop structure gives more robust performance. However, in literature, it has generally not been preferred due to the hardware complexity and cost imparted by the increase in the number of sensors. In this paper a three-loop control structure is proposed for grid connected inverters and is analysed for stability and parametric variations. Robustness of three-loop control as compared to a single-loop grid-current feedback control is studied. An approach to estimate the state variables is proposed in this paper which cuts down the number of sensors and cost of implementation. The algorithm is validated through MATLAB/SIMULINK results, under dynamic and steady state conditions.

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 score0.956
Threshold uncertainty score0.324

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 head0.011
GPT teacher head0.214
Teacher spread0.203 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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