A Practical Approach for Distribution Transformer Loss of Life Assessment Considering Electric Vehicles Penetration
Bibliographic record
Abstract
The rapid and massive acceptance of electric vehicles (EVs) is causing challenges for distribution transformers (DTs) to operate over their expected lifespan. Unlike substation power transformers, whose life consumed/loss of life (LOL) is monitored by directly measuring winding temperature, the winding temperature of DTs cannot be measured in practice due to their massive population, inexpensive cost, and lack of remote communication. Thus, the LOL of residential DTs are required to be estimated by IEEE Standard C57.91-2011. This work proposes a hardware-free two-stage practical approach to assess the real-time LOL of a distribution transformer in residential premises. The first stage determines the kVA load of a DT, without the need of fixed power factor assumption. The DT kVA load along with the ambient temperature and DT thermal parameters are used for DT LOL assessment. Numerical validation is conducted on real-world data utilizing electricity consumption and ambient temperature of fifteen households in London, Ontario, Canada. The study in this work also includes the penetration of the most popular EVs in Canada, along with the service drop cable data as well as practical secondary distribution circuit configuration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".