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Record W4405188625 · doi:10.37394/232030.2024.3.8

Inverter Coupled Energy Storage System for Soft-Restarting of Power System Dynamic Load

2024· article· en· W4405188625 on OpenAlexaff
Vikramsingh R. Parihar, Rohan V. Thakur, Mohan B. Tasare, Harshada M. Raghuwanshi

Bibliographic record

VenueInternational Journal on Applied Physics and Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsTrinity College
Fundersnot available
KeywordsInverterEnergy storageComputer scienceElectric power systemPower (physics)Reliability (semiconductor)GridDistributed generationReliability engineeringVoltageAutomotive engineeringElectrical engineeringEngineeringRenewable energy

Abstract

fetched live from OpenAlex

This paper presents the design and implementation of an inverter-coupled energy storage system (ESS) for the soft-restarting of dynamic loads in power systems, utilizing MATLAB Simulink as the simulation platform. The proposed system aims to enhance the reliability and stability of power grids by providing a controlled and efficient method for restarting loads after an interruption. The inverter-coupled ESS integrates a battery storage unit with a power inverter to supply the necessary energy for a smooth load restart, minimizing the impact on the overall system. Simulation results demonstrate the effectiveness of the system in maintaining voltage stability, reducing inrush currents, and ensuring a seamless transition during the load restarting process. The study highlights the potential of inverter-coupled ESS in modern power systems, offering a robust solution for managing dynamic load behavior and improving grid resilience.

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 score0.958
Threshold uncertainty score0.527

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)0.0000.000

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 head0.003
GPT teacher head0.176
Teacher spread0.173 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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