Delta-Type Serial Shunt Soft Normally-Open Points With Wide Power Flow Regulation Range in Distributed Network
Bibliographic record
Abstract
The soft normally-open points (SNOP) have been proposed in response to challenges raised from renewable energy in the grid. Main scheme adopted in SNOP demonstration projects are back-to-back modular multilevel converter (BTB-MMC), which is costly in multifeeder scenarios. The series shunt multiport SNOP (S2-MSNOP) can reduce the cost and volume. But its energy balance faces difficulties under operating conditions where reactive power provided by the cascaded H-bridge of the device is too small, thus impeding its practical implementation. Accordingly, this article presents a novel delta-type serial shunt soft-normally open points (D-S3NOP) scheme, offering a practical multifeeder interconnection solution. D-S3NOP employs an internal circulating current mechanism to achieve energy balance among its constituent parts, thereby expanding the power flow regulation range. First, the topology feature and working principle of D-S3NOP are presented. Subsequently, a suitable control strategy is designed. Comparison of power flow regulation area and cost among S2-MSNOP, D-S3NOP, and BTB-MMC is made after. Finally, a simulation model and an experimental prototype are constructed for verifying the effectiveness of D-S3NOP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".