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Record W4320712893 · doi:10.1109/access.2023.3244826

Experimental Validation of a Mitigation Method of Ferranti Effect in Transmission Line

2023· article· en· W4320712893 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 Access · 2023
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsOntario Tech University
FundersQatar National LibraryPalestine Technical University Kadoorie
KeywordsTransmission lineComputer scienceVoltageElectric power transmissionShunt (medical)Transmission (telecommunications)Control theory (sociology)Compensation (psychology)Electric power systemPower (physics)Electronic engineeringSimulationElectrical engineeringControl (management)EngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Electric power transmission networks should be operated in efficient, safe, and reliable conditions. To improve the stability and transfer capability of power transmission, it is necessary to mitigate the Ferranti effect. This paper investigates the impact of increasing the length of the transmission line on its receiving end voltage under no-load conditions. A variable shunt reactor compensation for transmission lines is used to control the voltage level at different lengths of the transmission line. The proposed method demonstrates that the value of the shunt reactor required to maintain the receiving end voltage can be estimated. Moreover, the system is modeled using the PowerWorld simulator, and the effectiveness of the proposed model has been verified by experimental results. The experimental results demonstrate the efficiency of the proposed methodology and match the simulation results, which are then validated by simulating the WSCC 9-bus and IEEE 30-bus test 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.243

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 head0.030
GPT teacher head0.350
Teacher spread0.320 · 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