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Record W2994916052 · doi:10.1109/iecon.2019.8926890

Active Damping of LCL Filter Resonance for Grid-Connected Distributed Power Generation Systems

2019· article· en· W2994916052 on OpenAlexaff
Benyu Zou, Alireza Bakhshai, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsControl theory (sociology)Band-stop filterHarmonicsActive filterPulse-width modulationInverterEngineeringVoltage-controlled filterController (irrigation)Filter (signal processing)SidebandDistributed generationLow-pass filterComputer scienceElectronic engineeringVoltageElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

PWM voltage source converters (VSCs) are increasingly used for connecting to the power grid for small Distribute Power Generation System (DPGS). A high-order low-pass LCL filter is adopted to get rid of the PWM carrier and sideband harmonics. However, LCL filter bring undesired resonance effect which generate stability problems for grid current control. Passive and Active damping are used to eliminate the resanonce peak. Passive damping reduces the filter effectiveness and also brings power loss issue. This paper presents a new systemmatic design approach for a single phase grid-connected Distributed Power Generation System (DPGS) inverter current control loop that actively damps the LCL filter resonance frequency while keeping inverter control variable well regulated. Two current control structures: PI control in synchronous reference frame (SRF) and PR control in stationary frame are adopted in cascade with a notch filter in current control loop. Details of the proposed active damping along with current controller design, mathematical modeling the LCL filter, simulations and experiment results are presented and discussed.

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 score0.645
Threshold uncertainty score0.298

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 head0.007
GPT teacher head0.184
Teacher spread0.177 · 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
Published2019
Admission routes1
Has abstractyes

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