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Record W2615368580 · doi:10.1109/apec.2017.7931098

A fast and accurate MPPT control technique using boundary controller for PV applications

2017· article· en· W2615368580 on OpenAlexaff
Yang Zhou, Carl Ngai Man Ho, Ken King Man Siu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaximum power point trackingTackingPhotovoltaic systemControl theory (sociology)Controller (irrigation)Computer scienceMaximum power principleRenewable energyPower (physics)Control engineeringEngineeringControl (management)Electrical engineeringPhysicsInverter

Abstract

fetched live from OpenAlex

Solar energy is one of the most commonly used renewable energy in the world. A Maximum Power Point Tracking (MPPT) control algorithm with appropriated converter are essential to insure the efficiency of solar energy utilization in photovoltaic (PV) applications. PI controller is widely used in MPPT controller due to easiness and low cost but not providing fast and accurate tracking. In this paper, a second-order switching surface control technique is applied in a MPPT controller to extract maximum power from PV cells. The proposed system can give a fast action of tacking the MPP when the irradiation of sun suddenly alters. The operating principles of the proposed control technique and mathematical derivations of the control law are provided in the paper. Simulation result shows that the proposed controller has a dramatic improvement in terms of dynamic response comparing with a conventional PI based MPPT controller. A 100 W prototype has been built and experiment results show a good agreement with the derived theory.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.917
Threshold uncertainty score0.655

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 studies0.0010.000
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 head0.025
GPT teacher head0.308
Teacher spread0.283 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations3
Published2017
Admission routes1
Has abstractyes

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