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Record W2889041299 · doi:10.1109/ccece.2018.8447644

A New Control Strategy for Power Quality Improvement Using MPPT from Hybrid PV-Wind Connected to the Grid

2018· article· en· W2889041299 on OpenAlexaff
Seghir Benhalima, Ambrish Chandra, Miloud Rezkallah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaximum power point trackingPhotovoltaic systemControl theory (sociology)Total harmonic distortionWind powerVoltage sourcePower optimizerComputer sciencePermanent magnet synchronous generatorEngineeringVoltageElectrical engineeringInverterControl (management)

Abstract

fetched live from OpenAlex

In this paper, robust and simple control strategy is presented for power quality improvement of a grid connected renewable energy based the maximum power point tracking (MPPT) from solar photovoltaic (PV) and wind based permanent magnet synchronous generator (PMSG) sources. A combination of MPPT control and active power control algorithms are applied to two level inverters for interfacing between PV-PMSG energy sources and the grid. For MPPT, dc-dc boost and AC-DC converters are used for PV system and wind turbine, respectively. Both these renewable sources are connected at the voltage source converter (VSC) to feed the grid. VSC is controlled for PQ improvement and AC grid voltage regulation. The developed control algorithms for a grid connected VSC based on modified synchronous reference frame (SRF), its use for desired power quality, minimize the total harmonic distortion (THD) and quick dc-link voltage regulation. The performance, effectiveness and the robustness of the hybrid system are validated using Matlab/Simulink. The proposed composite controller ensures rapid and desired control action without any adjustment during varying climate conditions and load variations.

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 score0.863
Threshold uncertainty score0.686

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)0.0010.000

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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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
Published2018
Admission routes1
Has abstractyes

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