Performance assessment and optimization strategy of new energy distribution network fault self-healing system based on Monte Carlo simulation
Bibliographic record
Abstract
The distribution network system is directly connected to users, and its faults directly affect people's daily life and economic activities.In order to realize rapid fault location, isolation and self-healing, this paper carries out a new energy distribution system reliability assessment based on sequential Monte Carlo simulation algorithm.Comparing the use of sequential simulation algorithms, non-sequential simulation algorithms and pseudo-sequential simulation algorithms in the state analysis of distribution network systems, the sequential Monte Carlo method is selected to establish a simulation model for distribution network reliability assessment.According to the "2-3-6" architecture system commonly used in distribution networks, a new energy distribution network self-healing system data communication scheme is designed.The Monte Carlo simulation method is used to model and analyze the reliability of new energy coupled components and to evaluate the reliability indexes.The simulation environment is designed and the RTDS model is used to test the self-healing system of intelligent devices in the distribution network.In the new energy distribution network data scheme designed in this paper, the distribution network self-healing technology is able to remove the fault time within 95ms and complete the fault point elimination work.And when the DG can make the far power side and the switch is located in the near power side, the distribution self-healing system is able to remove the fault point within 60ms time and complete the self-healing operation of the distribution network fault.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".