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Record W4311164163 · doi:10.18280/ts.390517

Analysis and Comparison of Performance of Interline Power Flow Controller with Various Control Algorithms under Various Power Stability Problems

2022· article· en· W4311164163 on OpenAlexvenueno aff
Bhavya Kanchanapalli, Ravi Srinivas Lanka

Bibliographic record

VenueTraitement du signal · 2022
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsnot available
Fundersnot available
KeywordsVoltage sourceControl theory (sociology)Flexible AC transmission systemController (irrigation)AC powerElectric power systemEngineeringVoltage controllerVoltageElectric power transmissionTransmission linePower (physics)Electronic engineeringComputer scienceElectrical engineeringPower flowVoltage droop

Abstract

fetched live from OpenAlex

The Interline Power Flow Controller (IPFC) is a voltage source converter based Flexible AC Transmission System (FACTS) controller for series compensation and power flow management among a substation's multiline transmission systems. Individual Voltage Source Converters (VSC) can inject reactive voltage that can be adjusted to manage active power flow in a line. This VSC is used to convert DC voltage to AC voltage and the voltage is kept constant in the entire process. In this article, a circuit model for IPFC is constructed, and a simulation of an interline power flow controller is performed, with control performed utilizing a variety of algorithms, including adaptive weighted feedback, gravitational search, BAT, and ANT colony optimization. The system's performance was evaluated in a variety of scenarios, including fault incidence, synchronous load connection, and asynchronous load connection. The design of system and analysis of system has been carried out using MATLAB Simulink in terms of various parameters at point of common coupling like voltage, current, power and power factor.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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