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Record W2786004902 · doi:10.1109/epec.2017.8286185

Nonlinear reactive power control scheme to maximize penetration of distributed generation in distribution networks

2017· article· en· W2786004902 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDistributed generationAC powerPhotovoltaic systemComputer scienceMATLABVoltageGridGreenhouse gasElectricity generationVoltage regulationEngineeringPower (physics)Electrical engineeringRenewable energy

Abstract

fetched live from OpenAlex

Distributed generation (DG) using solar photovoltaic (PV) as a source of energy is of interest to governments and utilities due to its ability to displace significant greenhouse gas (GHG) emissions while leveraging existing distributed generation infrastructure. This has led to increased PV installation across the grids in many countries. It can, however, lead to issues with distributed feeder voltage regulation and, ultimately voltage stability problems. The ability to maintain grid voltage stability is compromised by the addition of DG units in existing feeder networks. In this paper, a reactive power control technique is proposed that uses the concept of optimizing reactive power to reduce the problem of voltage instability and in doing so, provides a method to maximize the active power injected for each distributed generation unit connected to the grid. The algorithm is tested in MATLAB/Simulink with IEEE 13 and 37-node test feeder systems.

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 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 score0.520
Threshold uncertainty score0.760

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Quick stats

Citations4
Published2017
Admission routes1
Has abstractyes

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