Decentralized Topology Reconfiguration in Multiphase Distribution Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The cyber-physical nature of the modern power grid allows active power entities to exchange information signals with one another to make intelligent local actuation decisions. Exacting effective coordination amongst these cyber-enabled entities by way of strategic signal exchanges is essential for accommodating highly fluctuating power components (e.g., renewables, electric vehicles, etc.) that are becoming prevalent in today's electric grid. As such, in this paper, we present a novel decentralized topology reconfiguration algorithm for the distribution network (DN) that allows the system to adapt in real time to unexpected perturbations and/or congestions to restore balance in loads across the feeder and improve the DN voltage profile. For this, individual agents residing in DN buses iteratively exchange signals with neighbouring nodes to infer the current state (e.g., power balance and voltage) of the system and utilize this information to make local line switching decisions. Strong convergence properties and optimality conditions of the proposed algorithm are established via theoretical studies evoking potential games and discrete concavity. Comparative simulation studies conducted on realistic DNs showcase the practical properties of the proposed algorithm.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it