A Coordinated Restoration Method of Hybrid AC/DC Distribution Network for Resilience Enhancement
Bibliographic record
Abstract
In recent years, the frequent occurrence of extreme natural disasters has caused huge economic losses, which makes it extremely important to improve the resilience of distribution networks. With the increasing penetration of DC sources and loads, the urban distribution network is transitioning from AC to hybrid AC/DC configuration that can operate in a ring structure. The features of flexible interconnections and low network losses of DC lines can break the bottleneck of traditional restoration methods for AC distribution networks under extreme disasters, thereby further enhancing the resilience of distribution networks. Based on the interconnection feature of DC lines, this paper proposes a topology search strategy with DC lines as the core to realize the joint recovery of multiple power sources and multiple critical loads. With the obtained interconnection topology after topology search, a fault restoration model for maximizing the resilience index is established. To ensure the generality of the proposed model and explore the advantages of flexible DC power control, this paper transforms the objective function from the complex model into a mixed integer second-order cone programming (MISOCP) that can be solved directly. The optimal restoration strategy for resilience enhancement of AC/DC hybrid distribution networks can be obtained by solving the proposed MISOCP model. The numerical results in case study validate the effectiveness and superiority of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".