MétaCan
Menu
Back to cohort
Record W2536482265 · doi:10.1109/smart.2015.7399267

Power factor improvement in WECS using cascade PI control of passive damping LCL-filter

2015· article· en· W2536482265 on OpenAlexaff
Faris Hamoud, Mamadou Lamine Doumbia, A. Chériti

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsControl theory (sociology)Total harmonic distortionCascadeFilter (signal processing)ResistorPower factorActive filterBand-stop filterLow-pass filterVoltageEngineeringComputer scienceControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

This work proposes an algorithm for the elimination of switching ripples caused by the commutation of power semiconductors used in STATCOM. LCL-filter is a favorite solution to achieve this goal; however it reduces the stability of the system with the presence of resonance phenomena. Passive damping resistor can be inserted in series with capacity of LCL-filter to solve this problem. The LCL-filter is divided into three parts in order to reduce the interaction between filter components and the complexity of the control algorithm. Moreover, in order to improve the dynamic of the system, a cascade PI control is proposed. Different design steps are presented with damping resonance phenomena using a resistor connected in series with the capacitance. Our investigations show that the Total Harmonic Distortion (THD) of voltage and current injected into the grid tends to zero. That means, the passive damping LCL-filter attenuates the major part of current ripples compared with results obtained by using L-filter.

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: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.401

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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207