Assessment and Mitigation of Temporary Overvoltages on Distribution Feeders with High Penetration of Distributed Energy Resources
Bibliographic record
Abstract
Distributed Energy Resources (DERs) are proliferating in distribution systems across most jurisdictions. As distribution system operators work towards interconnecting DERs, they experience new issues that require thorough assessment and mitigation. Among these issues, one such example is coping with intensified Temporary Overvoltage (TOV) on un-faulted phases that results from a system reconfiguration that causes the distribution system to no longer be effectively grounded. In particular, inverter-based DERs and/or certain preferred step-up transformer configurations lead to worse TOV levels than those experienced in typical distribution systems pre-DER connection. TOVs pose insulators and surge arrestors at risk. Currently, there remains a gap in distribution planning of most jurisdictions in formulating the aspects of performance grounding and adopting simple and practical measures to overcome the challenge. This paper provides an overview and analytical evaluation of TOVs in modern distribution systems with high DER penetration and introduces mitigation options, including assessment and design procedures of effective ground sources. Two real distribution systems of a Canadian electric utility are presented as case studies to illustrate the problem definition and proposed mitigation strategies adopted in the project execution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".