Overvoltages due to Synchronous Tripping of Plug-in Electric-Vehicle Chargers Following Voltage Dips
Bibliographic record
Abstract
Plug-in electric vehicle (PEV) charging equipment incorporates protection that ensures that grid disturbances do not damage the charger or vehicle. When the grid voltage sags below 80% of nominal, undervoltage protection is likely to disconnect the charging load from the grid. Most PEV charging will occur overnight, when non-PEV load is at a minimum. This paper argues that PEV voltage-sag response, when synchronized across large numbers of PEVs, could result in the loss of a significant proportion of the total load. It is shown that this load loss can lead to unacceptably high voltages once the initiating event has been cleared. This paper explores the nature of this voltage-rise phenomenon. Analysis tools are developed to assist in determining PEV loading conditions that demarcate acceptable postdisturbance voltage response from unacceptable outcomes. Two examples, based on standard distribution test systems, are used to illustrate PEV-induced overvoltage behavior, and demonstrate applications of the analysis tools.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".