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Record W2164873037 · doi:10.1109/pesmg.2013.6672234

Stability analysis and control of medium-voltage micro-grids with dynamic loads

2013· article· en· W2164873037 on OpenAlexaff
Alireza Kahrobaeian, Yasser Abdel‐Rady I. Mohamed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVoltage droopControl theory (sociology)GridVoltageAC powerElectric power systemAutomatic frequency controlNonlinear systemComputer sciencePower (physics)EngineeringVoltage regulatorControl (management)Electrical engineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The concept of micro-grid is gaining widespread acceptance in distributed power networks. Medium-voltage (MV) micro-grids will be subjected to high penetration level of dynamic loads (e.g. line-start induction motor (IM) loads). The highly-nonlinear IM dynamics that couple the active power, reactive power, voltage and supply frequency dynamics challenge the stability of MV droop-controlled micro-grids. However, detailed analysis, and more importantly, stabilization of MV micro-grids with IM loads are not reported in current literature. To fill-out this gap, this paper presents the stability analysis and stabilization of MV droop-controlled micro-grids with IM loads. The proposed model accounts for the impact of supply frequency dynamics associated with the droop-control mechanism to accurately link the micro-grid frequency dynamics to the motor dynamics. The complete small-signal model is used to assess the impact of the IM dynamics on the micro-grid stability as compared to the static load case. To stabilize the micro-grid system in the presence of IM loads, a two-degree-of-freedom active damping controller is proposed to stabilize the newly introduced oscillatory dynamics.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.784
Threshold uncertainty score0.661

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 head0.002
GPT teacher head0.153
Teacher spread0.151 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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