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Advanced DSOGI PLL with Adaptive Bandwidth for Improved Transient Performance of Grid Connected Inverter Control Systems

2024· article· en· W4403127077 on OpenAlexaff
Githmi Ranasinghe, Athula Rajapakse, Lalin Kotalawala

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsManitoba HydroUniversity of Manitoba
Fundersnot available
KeywordsPhase-locked loopInverterComputer scienceTransient (computer programming)Bandwidth (computing)GridTransient analysisControl theory (sociology)Electronic engineeringTransient responseControl (management)Electrical engineeringEngineeringTelecommunicationsVoltageJitterOperating systemMathematics

Abstract

fetched live from OpenAlex

This paper proposes an advanced modified Double Second Order Generalized Integrator (DSOGI) Phase-Locked Loop (PLL) tailored specially for inverter-based systems which uses decoupled control systems. The proposed enhancements incorporate a transient detector for temporarily freezing the PLL frequency, which is used within the DSOGI-PLL control system, during transients. Additionally, an adaptive bandwidth technique dynamically adjusts the PLL bandwidth, ensuring swift response and reduced phase error during disturbances. The study underscores the importance of these modifications in achieving rapid and accurate synchronization, especially in inverter-based systems. Simulation results validate the effectiveness of the proposed method, showcasing its potential to mitigate instability issues and enhance system resilience when connecting inverter-based resources to weak grids.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.003
GPT teacher head0.158
Teacher spread0.155 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2024
Admission routes1
Has abstractyes

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