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Record W1941173606 · doi:10.1109/ecce.2015.7309721

A multifunctional grid tied solar energy conversion system with ANF based control approach

2015· article· en· W1941173606 on OpenAlexaff
Bhim Singh, Chinmay Jain, Sagar Goel, Ambrish Chandra, Kamal Al‐Haddad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsHarmonicsPhotovoltaic systemControl theory (sociology)Total harmonic distortionComputer scienceGridElectronic engineeringMaximum power point trackingAC powerEngineeringElectrical engineeringVoltageInverterMathematicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a two stage, three-phase grid interfaced SPV (Solar Photovoltaic) energy conversion system with an ANF (Adaptive Notch Filter) based control algorithm. The proposed SPV system is a multi-function grid-interfaced solar PV energy conversion system, which along with conversion of dc-power from SPV to AC mains, is capable of reactive power compensation, harmonics current elimination and load balancing in a three-phase AC distribution system. Compared to multiple devices with different functionalities, a multifunction grid-interfaced SPV energy conversion system is capable to save substantially capital investment, space and maintenance cost. The ANF successfully extracts a single sinusoid of a possibly non-stationary nature from harmonics corrupted load currents at PCC (Point of Common Coupling). The control algorithm is adaptive with respect to the fundamental frequency of the system compared to other methods; and provides instantaneous values of the fundamental signals. Simulation and experimental results verify the validity of the presented algorithm and confirms its desirable transient and steady state performances. The THD (Total Harmonics Distortion) of grid currents is found well under IEEE-519 standard even under nonlinear loads.

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 score0.975
Threshold uncertainty score0.777

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 head0.015
GPT teacher head0.192
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

Citations9
Published2015
Admission routes1
Has abstractyes

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