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Record W4386164025 · doi:10.14447/jnmes.v26i3.a03

Performance Analysis of Balanced Integrated Standalone Microgrid under Dynamic Load Conditions

2023· article· en· W4386164025 on OpenAlexvenueno aff
Anand Kumar Myla, Srinivasa Rao Gorantla

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogridComputer scienceReliability engineeringEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Standalone Microgrid has implemented in Mat-lab/Simulink platform, which has two Dispersed generation units based on a Solar Photovoltaic Generation and Wind Turbine Generation, how to get the most of wind and solar energy by lowering investment and operating expenses based on load power demand.The goal is to reduce one-time investment and operation expenses over the lifecycle; the limits are utilization rate and power supply reliability.The proposed system advantages, if Solar Photovoltaic Generation is absent, Wind Turbine Generation meets the load power demand, in case of a crucial situation, if Wind Turbine Generation is also absent, then Energy Storage System meets the load power demand, which means the power supply is reliable to the remote areas/limited load demand, besides in proposed system converters are reduced, so investment reduces.Mathematical models of Solar Photovoltaic Generation and Wind Turbine Generation are presented, to achieve Maximum Power Point Tracking of a Solar Photovoltaic Array, the Perturb and Observe method is used.Proportional-Integral controller used for Wind Turbine Generation, Solar Photovoltaic Generation, and Energy Storage System-based Direct Current/Direct Current Bi-directional Converter.An effective inductance filter is used for the mitigation of harmonics in the system.The finest simulation results are obtained, it concludes the system is in balanced condition under various loads with the Proportional-Integral controller and current total harmonic distortions are within limits.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.228
Teacher spread0.222 · 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
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

Citations4
Published2023
Admission routes1
Has abstractyes

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