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Record W3081478801 · doi:10.1109/tsg.2020.3018633

Distributed Coordinated Reactive Power Control for Voltage Regulation in Distribution Networks

2020· article· en· W3081478801 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2020
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAC powerDistributed generationVoltage regulationVoltageControl theory (sociology)Voltage optimisationControl (management)Renewable energyComputer scienceControl engineeringPower (physics)EngineeringDistributed computingElectrical engineering

Abstract

fetched live from OpenAlex

In this article, a novel distributed coordinated control framework is proposed to handle the uncertain voltage violations in active distribution networks. It addresses the problem of coordination of different types of devices in a distributed manner. In our control design, on-load tap changers (OLTCs) are firstly employed to handle the potential voltage violations based on the prediction of renewable outputs and load variations. During real-time operation, once an unmanageable voltage violation is detected, the reactive power of distributed energy resources (DERs) will be coordinated immediately to provide fast corrective control. The control schedules of OLTCs are calculated by solving a multitime-step constrained optimization problem via the alternating direction method of multipliers, whereas the reactive power injections of DERs are determined by a novel online distributed algorithm. The effectiveness of the proposed control framework is verified on the modified IEEE 34-bus and 123-bus test feeders.

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.

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)
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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.204
Teacher spread0.196 · 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