MétaCan
Menu
Back to cohort
Record W2767628838 · doi:10.1109/ias.2017.8101796

Frequency adaptive pre filtering stage for differentiation based control of shunt active filter under polluted grid conditions

2017· article· en· W2767628838 on OpenAlexaff
Sanchit Mishra, Ikhlaq Hussain, Bhim Singh, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTotal harmonic distortionControl theory (sociology)Active filterIntegratorHarmonicsComputer scienceAdaptive filterAC powerElectronic engineeringVoltageDistortion (music)Filter (signal processing)Electronic filterWaveformEngineeringBandwidth (computing)Electrical engineeringTelecommunicationsControl (management)Artificial intelligenceAmplifier

Abstract

fetched live from OpenAlex

A differentiation based control method for a shunt active power filter is used in this paper for functioning in the presence of grid voltage distortion and imbalance. To enable the fast and accurate estimation of the variables in the presence of unbalancing or distortion in the grid voltages, a pre-filtering scheme is considered based on the dual second order generalized integrator (DSOGI) approach. The signal recomposition performed by the pre-filter isolates the harmonics from the voltage and current waveforms, and the differentiation FLL (dFLL) is able to estimate the frequency of the waveforms which is used as a feedback for the pre-filter, thus making the system frequency adaptive. The control algorithm is validated with both simulation studies and a hardware prototype of a active power filter carrying out current compensation robustly, demonstrating the speed and accuracy of the system under grid voltage distortions and maintaining less than 5% supply current total harmonic distortion THD as dictated by IEEE Std. 519-2014.

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 score0.963
Threshold uncertainty score0.548

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 head0.014
GPT teacher head0.230
Teacher spread0.216 · 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
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207