MétaCan
Menu
Back to cohort
Record W4248044646 · doi:10.32920/ryerson.14648412

Voltage stability analysis using STATCOM

2021· preprint· en· W4248044646 on OpenAlexaff
Umang Patel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAC powerElectric power systemControl theory (sociology)Voltage optimisationPower-flow studyVoltage regulationVoltageTransmission systemPower (physics)EngineeringMATLABVolt-ampere reactiveFlexible AC transmission systemTransmission (telecommunications)Computer sciencePower flowControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

Power system stability is gaining importance because of unusual growth in power system. Day by day use of nonlinear load and other power electronics devices created distortions in the system which creates problems of voltage instability. Voltage stability of system is major concerns in power system stability. When a transmission network is operated near to their voltage stability limit it is difficult to control active-reactive power of the system. Our objectives are the analysis of voltage stability margin and active-reactive power control in proposed system which includes model of STATCOM with aim to analyse its behavior to improve voltage stability margin and active-reactive power control of the system under unbalanced condition. The study has been carried out using MATLAB Simulation program on three phase system connected to unbalanced three phase load via long transmission network and results of voltage and active-reactive power are presented. In future work, we can do power flow calculation of large power system network and find the weakest bus of the system and by placing STATCOM at that bus we can improve over all stability of the system

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 categoriesMeta-epidemiology (narrow), Insufficient 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: none
Teacher disagreement score0.763
Threshold uncertainty score1.000

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)0.0040.000

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 head0.034
GPT teacher head0.256
Teacher spread0.223 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPower System Optimization and StabilityFrench-language works237,207