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Record W4409804515 · doi:10.59019/wqyq7474

Evaluating the Effectiveness of Refractometric Sensors for Transformer Oil Ageing Monitoring

2024· dissertation· en· W4409804515 on OpenAlexaboutno aff
Ugochukwu Elele

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicPower Transformer Diagnostics and Insulation
Canadian institutionsnot available
Fundersnot available
KeywordsTransformerAgeingEnvironmental scienceEngineeringReliability engineeringComputer scienceElectrical engineeringMedicineVoltageInternal medicine

Abstract

fetched live from OpenAlex

With the growing emphasis on online ageing detection methodologies for transformer oil—a critical component of high-voltage networks that provides insulation, cooling, and state-of-health information for transformers—there is increasing recognition of their advantages over traditional offline methods. Offline techniques are often limited by challenges such as sample contamination, misinterpretation of results, exposure of on-site personnel to hazards, loss of man-hours, high costs associated with all five stages of detection, and their restriction to scheduled maintenance practices. Consequently, online ageing sensors for transformer oil are gaining significant traction in the field of high-voltage instrumentation. This study evaluates the effectiveness of refractometric optical fibre sensors for ageing detection in transformer oil, leveraging the advantages of fibre optic sensors, including immunity to high-voltage interference, lightweight design, and high sensitivity compared to other sensor types, such as cross-capacitance sensors. The research utilised intensity-modulated fibre optic sensors with varying sensing lengths, as well as the FISO Quebec Fabry-Pérot phase-modulated optical fibre sensor and Universal Multichannel Instrument. Industrial transformer oil samples—including Miden eN 1204 natural ester oil, Polaris GX mineral oil, Nytro Bio 300X vegetable oil, and Midel eN 7131 synthetic ester oil—were degassed, dried, and subjected to accelerated thermal ageing at 115°C. These samples were then characterised for key ageing markers, including interfacial tension, total acidity number (TAN), dissolved decay products (via UV-Vis spectroscopy), particle count, turbidity, and moisture content. The sensitivity analysis revealed that intensity-modulated optical fibre sensors are influenced by the length of the sensing region and the wavelength of the optical source, and they exhibit lower repeatability compared to phase-modulated optical fibre sensors. The FISO Quebec Fabry-Pérot phase-modulated optical fibre sensor demonstrated superior performance, with outputs that are less affected by twists and a rapid settling time of under 0.5 minutes across all four transformer oil types. Thermal response analysis using the FISO Quebec Fabry-Pérot sensor, which has a thermal coefficient of 0.0004 RIU/°C, indicated that the natural ester oil (NE 1204) and Nytro Bio 300X vegetable oil exhibited better thermal stability compared to Polaris GX mineral oil and synthetic ester oil. The enhanced thermal response properties of Nytro Bio 300X were attributed to the presence of antioxidants. Furthermore, correlation analysis between ageing markers from the aged transformer oil samples and the refractive index outputs of the FISO Quebec Fabry-Pérot sensor confirmed its reliability for identifying ageing markers in NE 1204 natural ester oil and Nytro Bio 300X. This reliability is evidenced by a high linear coefficient of determination exceeding 90% and low prediction errors of about 10% for moisture, interfacial tension, and turbidity when validated with independent datasets.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.028
GPT teacher head0.337
Teacher spread0.309 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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