MétaCan
Menu
Back to cohort
Record W4365129424 · doi:10.1109/tpel.2023.3266300

Efficient MPPT for BLDCM-Driven PV Pumping System Based on Ripple Correlation Control

2023· article· en· W4365129424 on OpenAlex
Amir Khazaee, Amirnaser Yazdani, Hamidreza Mosaddegh Hesar, Bin Wu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2023
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRippleControl theory (sociology)Maximum power point trackingPhotovoltaic systemControl systemCorrelationPhysicsControl (management)Computer scienceEngineeringElectrical engineeringMathematicsVoltageInverterArtificial intelligence

Abstract

fetched live from OpenAlex

A fast and parameter-intensive maximum power point tracking (MPPT) technique is presented for a photovoltaic (PV) water pumping system, driven by the brushless dc motor (BLDCM). The technique is based on the application of “ripple correlation control”. As an advantage, rather than artificial signal injection, the proposed technique employs the inherent perturbations introduced due to phase commutation and nonsinusoidal back-EMF of BLDCM to determine the maximum power points of the PV system. The technique can be simply employed for different configurations of BLDCM-based PV water pumping systems. Experiments are performed to demonstrate the effectiveness of the proposed technique.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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