Understanding the Relationship Between Insulation Aging and Gassing Tendency of Some Biodegradable Dielectric Fluids
Bibliographic record
Abstract
This study investigates the degradation mechanisms and gas generation behaviors of three biodegradable dielectric fluids, bio-based mineral oils (Bio-MO), natural esters (NE), and synthetic esters (SE), under electrical stress, focusing on their application in power transformers. The research adopts an extended experimental protocol that includes multiple aging durations (250–2000 hours) and repeated breakdown voltage (BDV) tests to simulate arc fault conditions and ensure reproducible trend analysis. Key characterizations such as Dissipation Factor (DDF), Interfacial Tension (IFT), and Total Acid Number (TAN) were combined with Dissolved Gas Analysis (DGA) to monitor fluid degradation. A novel contribution of this work is the statistical correlation framework introduced to quantify the relationships between increases in gases (%C2H2, %TDCG) and aging markers using correlation coefficients (r, p-value). Furthermore, Duval’s diagnostic tools (Triangle 1/Pentagon 1 for Bio-MO, Triangle 3/Pentagon 3 for esters) revealed that fault types evolve with aging. Specifically, Bio-MO transitioned from high-energy (D2) to low-energy (D1) discharges, strongly correlated with rising %DDF and %TAN. In contrast, NE and SE maintained stable D1 diagnostics. These findings offer a predictive approach to fault identification and transformer health assessment, paving the way for broader implementation with larger sample sets and field validation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".