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Record W2060269017 · doi:10.5370/jicee.2013.3.4.340

Effective Voltage Control by SVR to Reduce the Capacity of SVC using Solar Radiation Information with Real Time Simulator

2013· article· en· W2060269017 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

VenueJournal of International Council on Electrical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsOpal-Rt Technologies (Canada)
Fundersnot available
KeywordsPhotovoltaic systemVoltageStatic VAR compensatorControl theory (sociology)Voltage regulatorComputer scienceVoltage regulationEngineeringAC powerControl (management)Electrical engineering

Abstract

fetched live from OpenAlex

With the increasing the number of Photovoltaic generators (PV) connected to distribution system (DS), several concerns such as rise and sudden change of voltage on distribution line are growing in Japan. Step Voltage Regulator (SVR) is well known as the one of voltage control equipment used in current DS. Meanwhile, SVR cannot regulate rapid voltage change because SVR has time delay against variation of voltage. In contrast, Static Var Compensator (SVC) is the effective device to control voltage changed rapidly. However, since the cost of SVC with large capacity is expensive, it is important to reduce the capacity of SVC in order to increase the introduction of SVC into power system. From this background, the novel control method of SVR using solar radiation information to reduce the capacity of SVC is proposed in this paper. The effectiveness of the proposed method is confirmed by numerical simulation with real time simulator.

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)0.001
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: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.186
Teacher spread0.180 · 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