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Record W4385068223 · doi:10.18282/iac.v6i1.569

Speed control of PMBLDC motor drive powered by solar PV array using P, PI, and PID controllers: A comparison study

2023· article· en· W4385068223 on OpenAlexaff
Satish Kumar Doniparthi, S. B. Ron Carter, Amit V. Sant, Ali Moghassemi

Bibliographic record

VenueInsight - Automatic Control · 2023
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsPID controllerControl theory (sociology)DC motorDSPACEElectronic speed controlMATLABTorqueMagnetController (irrigation)Motor driveComputer scienceControl engineeringEngineeringElectrical engineeringPhysicsControl (management)Temperature controlMechanical engineering

Abstract

fetched live from OpenAlex

<p align="justify">Because of their high efficiency, better starting torque, and minimal electrical noise, permanent magnet brushless DC (PMBLDC) motors are frequently used in a variety of industrial applications. The speed of PMBLDC motors is controlled by a variety of controllers. In this study, P, PI, and PID controllers are used to compare the speed control of a permanent magnet brushless DC motor drive powered by solar PV arrays. The Perturb & Observe (P&O) technique is used to find the MPPT. The drive system’s simulation results for various operation modes, such as constant and variable load circumstances, are examined and evaluated. When using a PID controller instead of a P or PI controller, the drive performs better at controlling speed. The MATLAB/Simulink software was used to model, control, and simulate the permanent magnet brushless DC motor drive. The whole drive system is put into operation with the help of the dSPACE MicroLab Box 1202.</p>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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.

Study designBench or experimental
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

Citations0
Published2023
Admission routes1
Has abstractyes

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