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Record W1990687669 · doi:10.1109/icrera.2014.7016571

Flicker mitigation planning solutions in distributed wind power: A real-time simulation analysis

2014· article· en· W1990687669 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

Venue2014 International Conference on Renewable Energy Research and Application (ICRERA) · 2014
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlickerWind powerTransformerComputer scienceEngineeringReliability engineeringSimulationVoltageElectronic engineeringAutomotive engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper studies the sensitivity of flicker severity to flicker mitigation planning solutions that can be adopted by distribution network operators. More specifically, the paper studies the flicker mitigation capacity under different bandwidths of direct voltage control stipulated by the network operator and implemented by a distributed wind farm, different MV/HV substation transformer ratings, series compensation of the distribution line and finally, the change of the wind farm connection point. The purpose of this paper is to provide results that help in the understanding of the feasibility and effectiveness of flicker mitigation solutions in the planning phase of a wind farm. A detailed typical North-American 25 kV distribution network is employed as the test network and a 10 MW DFIG-based wind farm is employed as the source of wind power. The flicker measurement is carried out for a 10-minute time frame according to the guidelines of IEC standard 61000-4-15. All studies are conducted on a multi-processor real-time simulator for its superior computational capacity.

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)0.001
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 score0.743
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.030
GPT teacher head0.316
Teacher spread0.286 · 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