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Record W2902340463 · doi:10.1109/tpwrd.2018.2882999

Wildfire Trips De-Energized Line Shunt Reactor

2018· article· en· W2902340463 on OpenAlexaff
Mukesh Nagpal, R. Barone, T.G. Martinich, Zhixian Jiao, Ska-Hiish Manuel, Steve Merriman

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsBC Hydro (Canada)
Fundersnot available
KeywordsOvercurrentVoltageElectrical engineeringLine (geometry)Electronic circuitGroundEngineeringOvervoltageShort circuitNuclear engineeringAutomotive engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Dense smoke from wildfires within a 500-kV line corridor led to several line protection trips over a three-day period. A switchable line-connected reactor, provided to control Ferranti voltages, tripped and locked out twice by its protection shortly after the line was de-energized. Lockout operation prevented quick reactor reinsertion and impeded operational flexibility until visual inspections of the reactor were conducted on both occasions. Forensic investigation of the event concluded that the smoke increased the line capacitances. This created a near 60-Hz zero sequence resonant circuit when the line tripped and became de-energized along with the reactor. The resonant circuit amplified the capacitively coupled zero sequence 60-Hz voltage induced from circuits which run adjacent to the line for a short distance. The voltage amplification drove enough current through the reactor to trip the sensitive ground overcurrent protection. A holistic mitigation is designed to avoid reoccurrence of the incident and improve overall line reliability. The intent of this paper is to share lessons learned from the event investigation with peer utilities because this problem of unexpected operation of line shunt reactor protection has not previously been reported.

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.

How this classification was reachedexpand

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.999

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)0.0020.000

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 head0.221
Teacher spread0.213 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2018
Admission routes1
Has abstractyes

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