On the Transient Behavior of Large-Scale Distribution Networks During Automatic Feeder Reconfiguration
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 paper presents an in-depth analysis of the automatic reconfiguration and self-healing principles of the next generation (3G) smart grid of a real metropolitan distribution network. The large network is to be divided dynamically and remotely controlled into three smaller subnetworks to further increase the reliability of electrical power distribution secondary networks. When one subsection is experiencing difficulties, there is no longer the need to de-energize the entire network. A time-domain (EMTP) model has been developed and validated by comparing simulations with recordings of actual transient events. Different switching and fault scenarios are investigated using this model. Analysis of the results provides important conclusions on equipment rating, relay protection coordination, voltage regulation, switching and operation strategies which are discussed in the paper. A subset of these results is presented for illustration. This extensive study of a complex urban network suggests that: 1) before implementation of smart grid principles, it would be prudent to supplement steady-state analysis with time-domain analysis to avoid problems, such as installation of improperly rated equipment, and improper relay-protection coordination; and 2) EMTP-type programs may be used to conduct the time-domain analysis, despite the enormous number of elements contained in an urban network.
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 | 0.000 |
| 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