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Record W1990747918 · doi:10.1109/tdei.2012.6215096

On-line monitoring of partial discharges in a HVDC station environment

2012· article· en· W1990747918 on OpenAlexaff
Nathan D. Jacob, W. McDermid, Behzad Kordi

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2012
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of ManitobaManitoba Hydro
Fundersnot available
KeywordsBushingPartial dischargeHVDC converterConvertersHVDC converter stationTransformerElectronic engineeringElectrical engineeringEngineeringElectromagnetic interferenceVoltageHigh voltageCondition monitoring

Abstract

fetched live from OpenAlex

This paper deals with the on-line measurement of partial discharges (PD) in a high voltage direct current (HVDC) converter station. The HVDC station is a particularly challenging environment for measurement due to elevated interference levels caused from the switching of thyristor-controlled converters. In this work, online PD measurements were performed on two specimens; one is an HVDC converter transformer and the other an HVDC converter wall bushing. The measurements were performed in the high-frequency range from 400 kHz to 30 MHz with modern wideband PD measurement instrumentation. Results demonstrate that on-line measurement of PD in an HVDC station environment is possible and that a combination of input filtering and modern signal processing methods for feature extraction can be used to mitigate the converter interference. The feature extraction method used plots partial discharge data on a time-frequency classification map. The map enabled isolation of individual PD phenomena, and demonstrated that trending to monitor insulation degradation is possible with online partial discharge measurement. Furthermore, specific strategies for PD measurement and analysis are developed for each of the transformer and bushing specimens.

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 categoriesnone
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 score0.224
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.271
Teacher spread0.240 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations39
Published2012
Admission routes1
Has abstractyes

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