Fast Path Recovery for Single Link Failure in SDN-Enabled Wide Area Measurement System
Bibliographic record
Abstract
In the wide area measurement system (WAMS), the end-to-end transmission delay between the phasor measurement unit (PMU) and phasor data concentrator (PDC) is strictly constrained for real-time monitoring and protection applications. When a communication link failure happens, fast path recovery is required to reduce the impact of measurement losses. In this work, the promising software-defined network (SDN) technique is leveraged to compute the re-routing path in a global view upon a single link failure. More specifically, a hybrid fast path recovery algorithm (HFPR-A) is proposed based on the principle of simplicity: in some cases, the shortest path or approximate shortest path between PMU and PDC can be recovered byadding only one edgeto the original forwarding tree; while in the other cases, the shortest paths can be recovered withlower computational complexitythan the traditional Dijkstra’s algorithm. The proposed HFPR-A is implemented on the Ryu + Mininet testbed, and the simulation results on different IEEE benchmark test power systems show that the proposed HFPR-A could find shorter re-routing paths than the existing methods with a low-enough response time.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".