A State-of-the-Art Review on the Potential of Waste Cooking Oil as a Sustainable Insulating Liquid for Green Transformers
Bibliographic record
Abstract
Petroleum-based insulating liquids have traditionally been used in the electrical industry for cooling and insulation. However, their environmental drawbacks, such as non-biodegradability and ecological risks, have led to increasing regulatory restrictions. As a sustainable alternative, vegetable-based insulating liquids have gained attention due to their biodegradability, non-toxicity to aquatic and terrestrial ecosystems, and lower carbon emissions. Adopting vegetable-based insulating liquids also aligns with United Nations Sustainable Development Goals (SDGs) 7 and 13, which focus on cleaner energy sources and reducing carbon emissions. Despite these benefits, most commercially available vegetable-based insulating liquids are derived from edible seed oils, raising concerns about food security and the environmental footprint of large-scale agricultural production, which contributes to greenhouse gas emissions. In recent years, waste cooking oils (WCOs) have emerged as a promising resource for industrial applications through waste-to-value conversion processes. However, their potential as transformer insulating liquids remains largely unexplored due to limited research and available data. This review explores the feasibility of utilizing waste cooking oils as green transformer insulating liquids. It examines the conversion and purification processes required to enhance their suitability for insulation applications, evaluates their dielectric and thermal performance, and assesses their potential implementation in transformers based on existing literature. The objective is to provide a comprehensive assessment of waste cooking oil as an alternative insulating liquid, highlight key challenges associated with its adoption, and outline future research directions to optimize its properties for high-voltage transformer applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".